NAG Library Function Document
nag_opt_sparse_convex_qp_solve (e04nqc)
Note: this function uses optional arguments to define choices in the problem specification and in the details of the algorithm. If you wish to use default
settings for all of the optional arguments, you need only read Sections 1 to 10 of this document. If, however, you wish to reset some or all of the settings please refer to Section 11 for a detailed description of the algorithm, to Section 12 for a detailed description of the specification of the optional arguments and to Section 13 for a detailed description of the monitoring information produced by the function.
1 Purpose
nag_opt_sparse_convex_qp_solve (e04nqc) solves sparse linear programming or convex quadratic programming problems. The initialization function
nag_opt_sparse_convex_qp_init (e04npc) must have been called before calling nag_opt_sparse_convex_qp_solve (e04nqc).
2 Specification
#include <nag.h> 
#include <nage04.h> 
void 
nag_opt_sparse_convex_qp_solve (Nag_Start start,
Integer m,
Integer n,
Integer ne,
Integer nname,
Integer lenc,
Integer ncolh,
Integer iobj,
double objadd,
const char *prob,
const double acol[],
const Integer inda[],
const Integer loca[],
const double bl[],
const double bu[],
const double c[],
const char *names[],
const Integer helast[],
Integer hs[],
double x[],
double pi[],
double rc[],
Integer *ns,
Integer *ninf,
double *sinf,
double *obj,
Nag_E04State *state,
Nag_Comm *comm,
NagError *fail) 

Before calling nag_opt_sparse_convex_qp_solve (e04nqc) or one of the option setting functions
nag_opt_sparse_convex_qp_init (e04npc) must be called.
#include <nag.h> 
#include <nage04.h> 
void 
nag_opt_sparse_convex_qp_init (Nag_E04State *state,
NagError *fail) 

After calling nag_opt_sparse_convex_qp_solve (e04nqc) you can call one or both of the functions
to obtain the current value of an optional argument.
3 Description
nag_opt_sparse_convex_qp_solve (e04nqc) is designed to solve largescale
linear or
quadratic programming problems of the form:
where
$x$ is an
$n$vector of variables,
$l$ and
$u$ are constant lower and upper bounds,
$A$ is an
$m$ by
$n$ sparse matrix and
$f\left(x\right)$ is a linear or quadratic objective function that may be specified in a variety of ways, depending upon the particular problem being solved. The optional argument
${\mathbf{Maximize}}$ may be used to specify a problem in which
$f\left(x\right)$ is maximized instead of minimized.
Upper and lower bounds are specified for all variables and constraints. This form allows full generality in specifying various types of constraint. In particular, the $j$th constraint may be defined as an equality by setting ${l}_{j}={u}_{j}$. If certain bounds are not present, the associated elements of $l$ or $u$ may be set to special values that are treated as $\infty $ or $+\infty $.
The possible forms for the function
$f\left(x\right)$ are summarised in
Table 1. The most general form for
$f\left(x\right)$ is
where
$q$ is a constant,
$c$ is a constant
$n$vector and
$H$ is a constant symmetric
$n$ by
$n$ matrix with elements
$\left\{{H}_{ij}\right\}$. In this form,
$f$ is a quadratic function of
$x$ and
(1) is known as a
quadratic program (QP). nag_opt_sparse_convex_qp_solve (e04nqc) is suitable for all
convex quadratic programs. The defining feature of a
convex QP is that the matrix
$H$ must be
positive semidefinite, i.e., it must satisfy
${x}^{\mathrm{T}}Hx\ge 0$ for all
$x$. If not,
$f\left(x\right)$ is nonconvex and nag_opt_sparse_convex_qp_solve (e04nqc) will terminate with the error indicator
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_HESS_INDEF. If
$f\left(x\right)$ is nonconvex it may be more appropriate to call
nag_opt_sparse_nlp_solve (e04vhc) instead.
Problem type 
Objective function $f\left(x\right)$ 
Hessian matrix $H$ 
FP 
Not applicable 
$q=c=H=0$ 
LP 
$q+{c}^{\mathrm{T}}x$ 
$H=0$ 
QP 
$q+{c}^{\mathrm{T}}x+\frac{1}{2}{x}^{\mathrm{T}}Hx$ 
Symmetric positive semidefinite 
Table 1
Choices for the objective function $f\left(x\right)$
If
$H=0$, then
$f\left(x\right)=q+{c}^{\mathrm{T}}x$ and the problem is known as a
linear program (LP). In this case, rather than defining an
$H$ with zero elements, you can define
$H$ to have no columns by setting
${\mathbf{ncolh}}=0$ (see
Section 5).
If $H=0$, $q=0$, and $c=0$, there is no objective function and the problem is a feasible point problem (FP), which is equivalent to finding a point that satisfies the constraints on $x$. In the situation where no feasible point exists, several options are available for finding a point that minimizes the constraint violations (see the description of the optional argument ${\mathbf{Elastic\; Mode}}$).
nag_opt_sparse_convex_qp_solve (e04nqc) is suitable for large LPs and QPs in which the matrix
$A$ is
sparse, i.e., when the number of zero elements is sufficiently large that it is worthwhile using algorithms which avoid computations and storage involving zero elements. The matrix
$A$ is input to nag_opt_sparse_convex_qp_solve (e04nqc) by means of the three array arguments
acol,
inda and
loca. This allows you to specify the pattern of nonzero elements in
$A$.
nag_opt_sparse_convex_qp_solve (e04nqc) exploits structure in $H$ by requiring $H$ to be defined implicitly in a function
that computes the product $Hx$ for any given vector $x$. In many cases, the product $Hx$ can be computed very efficiently for any given $x$, e.g., $H$ may be a sparse matrix, or a sum of matrices of rankone.
For problems in which
$A$ can be treated as a
dense matrix, it is usually more efficient to use
nag_opt_lp (e04mfc),
nag_opt_lin_lsq (e04ncc) or
nag_opt_qp (e04nfc).
There is considerable flexibility allowed in the definition of
$f\left(x\right)$ in
Table 1. The vector
$c$ defining the linear term
${c}^{\mathrm{T}}x$ can be input in three ways: as a sparse row of
$A$; as an explicit dense vector
$c$; or as both a sparse row and an explicit vector (in which case,
${c}^{\mathrm{T}}x$ will be the sum of two linear terms). When stored in
$A$,
$c$ is the
iobjth row of
$A$, which is known as the
objective row. The objective row must always be a
free row of
$A$ in the sense that its lower and upper bounds must be
$\infty $ and
$+\infty $. Storing
$c$ as part of
$A$ is recommended if
$c$ is a sparse vector. Storing
$c$ as an explicit vector is recommended for a sequence of problems, each with a different objective (see arguments
c and
lenc).
The upper and lower bounds on the
$m$ elements of
$Ax$ are said to define the
general constraints of the problem. Internally, nag_opt_sparse_convex_qp_solve (e04nqc) converts the general constraints to equalities by introducing a set of
slack variables $s$, where
$s={\left({s}_{1},{s}_{2},\dots ,{s}_{m}\right)}^{\mathrm{T}}$. For example, the linear constraint
$5\le 2{x}_{1}+3{x}_{2}\le +\infty $ is replaced by
$2{x}_{1}+3{x}_{2}{s}_{1}=0$, together with the bounded slack
$5\le {s}_{1}\le +\infty $. The problem defined by
(1) can therefore be rewritten in the following equivalent form:
Since the slack variables
$s$ are subject to the same upper and lower bounds as the elements of
$Ax$, the bounds on
$x$ and
$Ax$ can simply be thought of as bounds on the combined vector
$\left(x,s\right)$. (In order to indicate their special role in QP problems, the original variables
$x$ are sometimes known as ‘column variables’, and the slack variables
$s$ are known as ‘row variables’.)
Each LP or QP problem is solved using a twophase iterative procedure (in which the general constraints are satisfied throughout): a feasibility phase (Phase 1), in which the sum of infeasibilities with respect to the bounds on $x$ and $s$ is minimized to find a feasible point that satisfies all constraints within a specified feasibility tolerance; and an optimality phase (Phase 2), in which $f\left(x\right)$ is minimized (or maximized) by constructing a sequence of iterates that lies within the feasible region.
Phase 1 involves solving a linear program of the form
Phase 1 


$\underset{x,s,v,w}{\mathrm{minimize}}}\phantom{\rule{0.25em}{0ex}}{\displaystyle \sum _{j=1}^{n+m}}\left({v}_{j}+{w}_{j}\right)$ 

$\text{subject to}Axs=0\text{,\hspace{1em}}l\le \left(\begin{array}{c}x\\ s\end{array}\right)v+w\le u\text{,\hspace{1em}}v\ge 0\text{,\hspace{1em}}w\ge 0$ 
which is equivalent to minimizing the sum of the constraint violations. If the constraints are feasible (i.e., at least one feasible point exists), eventually a point will be found at which both
$v$ and
$w$ are zero. Then the associated value of
$\left(x,s\right)$ satisfies the original constraints and is used as the starting point for the Phase 2 iterations for minimizing
$f\left(x\right)$.
If the constraints are infeasible (i.e.,
$v\ne 0$ or
$w\ne 0$ at the end of Phase 1), no solution exists for
(1) and you have the option of either terminating or continuing in socalled
elastic mode (see the discussion of the optional argument
${\mathbf{Elastic\; Mode}}$). In elastic mode, a ‘relaxed’ or ‘perturbed’ problem is solved in which
$f\left(x\right)$ is minimized while allowing some of the bounds to become ‘elastic’, i.e., to change from their specified values. Variables subject to elastic bounds are known as
elastic variables. An elastic variable is free to violate one or both of its original upper or lower bounds. You are able to assign which bounds will become elastic if elastic mode is ever started (see the argument
helast in
Section 5).
To make the relaxed problem meaningful, nag_opt_sparse_convex_qp_solve (e04nqc) minimizes
$f\left(x\right)$ while (in some sense) finding the ‘smallest’ violation of the elastic variables. In the situation where all the variables are elastic, the relaxed problem has the form
Phase 2 ($\gamma $)



$\underset{x,s,v,w}{\mathrm{minimize}}}\phantom{\rule{0.25em}{0ex}}f\left(x\right)+\gamma {\displaystyle \sum _{j=1}^{n+m}}\left({v}_{j}+{w}_{j}\right)$ 

$\text{subject to}Axs=0\text{,\hspace{1em}}l\le \left(\begin{array}{c}x\\ s\end{array}\right)v+w\le u\text{,\hspace{1em}}v\ge 0\text{,\hspace{1em}}w\ge 0$,

where
$\gamma $ is a nonnegative argument known as the
elastic weight (see the description of the optional argument
${\mathbf{Elastic\; Weight}}$), and
$f\left(x\right)+\gamma {\displaystyle \sum _{j}}\phantom{\rule{0.25em}{0ex}}\left({v}_{j}+{w}_{j}\right)$ is called the
composite objective. In the more general situation where only a subset of the bounds are elastic, the
$v$'s and
$w$'s for the nonelastic bounds are fixed at zero.
The elastic weight can be chosen to make the composite objective behave like the original objective $f\left(x\right)$, the sum of infeasibilities, or anything inbetween. If $\gamma =0$, nag_opt_sparse_convex_qp_solve (e04nqc) will attempt to minimize $f$ subject to the (true) upper and lower bounds on the nonelastic variables (and declare the problem infeasible if the nonelastic variables cannot be made feasible).
At the other extreme, choosing $\gamma $ sufficiently large will have the effect of minimizing the sum of the violations of the elastic variables subject to the original constraints on the nonelastic variables. Choosing a large value of the elastic weight is useful for defining a ‘leastinfeasible’ point for an infeasible problem.
In Phase 1 and elastic mode, all calculations involving $v$ and $w$ are done implicitly in the sense that an elastic variable ${x}_{j}$ is allowed to violate its lower bound (say) and an explicit value of $v$ can be recovered as ${v}_{j}={l}_{j}{x}_{j}$.
A constraint is said to be active or binding at $x$ if the associated element of either $x$ or $Ax$ is equal to one of its upper or lower bounds. Since an active constraint in $Ax$ has its associated slack variable at a bound, the status of both simple and general upper and lower bounds can be conveniently described in terms of the status of the variables $\left(x,s\right)$. A variable is said to be nonbasic if it is temporarily fixed at its upper or lower bound. It follows that regarding a general constraint as being active is equivalent to thinking of its associated slack as being nonbasic.
At each iteration of an activeset method, the constraints
$Axs=0$ are (conceptually) partitioned into the form
where
${x}_{N}$ consists of the nonbasic elements of
$\left(x,s\right)$ and the
basis matrix $B$ is square and nonsingular. The elements of
${x}_{B}$ and
${x}_{S}$ are called the
basic and
superbasic variables respectively; with
${x}_{N}$ they are a permutation of the elements of
$x$ and
$s$. At a QP solution, the basic and superbasic variables will lie somewhere between their upper or lower bounds, while the nonbasic variables will be equal to one of their bounds. At each iteration,
${x}_{S}$ is regarded as a set of independent variables that are free to move in any desired direction, namely one that will improve the value of the objective function (or sum of infeasibilities). The basic variables are then adjusted in order to ensure that
$\left(x,s\right)$ continues to satisfy
$Axs=0$. The number of superbasic variables (
${n}_{S}$ say) therefore indicates the number of degrees of freedom remaining after the constraints have been satisfied. In broad terms,
${n}_{S}$ is a measure of
how nonlinear the problem is. In particular,
${n}_{S}$ will always be zero for FP and LP problems.
If it appears that no improvement can be made with the current definition of $B$, $S$ and $N$, a nonbasic variable is selected to be added to $S$, and the process is repeated with the value of ${n}_{S}$ increased by one. At all stages, if a basic or superbasic variable encounters one of its bounds, the variable is made nonbasic and the value of ${n}_{S}$ is decreased by one.
Associated with each of the $m$ equality constraints $Axs=0$ is a dual variable ${\pi}_{i}$. Similarly, each variable in $\left(x,s\right)$ has an associated reduced gradient ${d}_{j}$ (also known as a reduced cost). The reduced gradients for the variables $x$ are the quantities $g{A}^{\mathrm{T}}\pi $, where $g$ is the gradient of the QP objective function, and the reduced gradients for the slack variables $s$ are the dual variables $\pi $. The QP subproblem is optimal if ${d}_{j}\ge 0$ for all nonbasic variables at their lower bounds, ${d}_{j}\le 0$ for all nonbasic variables at their upper bounds and ${d}_{j}=0$ for all superbasic variables. In practice, an approximate QP solution is found by slightly relaxing these conditions on ${d}_{j}$ (see the description of the optional argument ${\mathbf{Optimality\; Tolerance}}$).
The process of computing and comparing reduced gradients is known as
pricing (a term first introduced in the context of the simplex method for linear programming). To ‘price’ a nonbasic variable
${x}_{j}$ means that the reduced gradient
${d}_{j}$ associated with the relevant active upper or lower bound on
${x}_{j}$ is computed via the formula
${d}_{j}={g}_{j}{a}_{j}^{\mathrm{T}}\pi $, where
${a}_{j}$ is the
$j$th column of
$\left(\begin{array}{cc}A& I\end{array}\right)$. (The variable selected by such a process and the corresponding value of
${d}_{j}$ (i.e., its reduced gradient) are the quantities
+SBS and
dj in the monitoring file output; see
Section 9.1.) If
$A$ has significantly more columns than rows (i.e.,
$n\gg m$), pricing can be computationally expensive. In this case, a strategy known as
partial pricing can be used to compute and compare only a subset of the
${d}_{j}$s.
nag_opt_sparse_convex_qp_solve (e04nqc) is based on SQOPT, which is part of the SNOPT package described in
Gill et al. (2005a). It uses stable numerical methods throughout and includes a reliable basis package (for maintaining sparse
$LU$ factors of the basis matrix
$B$), a practical antidegeneracy procedure, efficient handling of linear constraints and bounds on the variables (by an activeset strategy), as well as automatic scaling of the constraints. Further details can be found in
Section 11.
4 References
Fourer R (1982) Solving staircase linear programs by the simplex method Math. Programming 23 274–313
Gill P E and Murray W (1978) Numerically stable methods for quadratic programming Math. Programming 14 349–372
Gill P E, Murray W and Saunders M A (1995) User's guide for QPOPT 1.0: a Fortran package for quadratic programming Report SOL 954 Department of Operations Research, Stanford University
Gill P E, Murray W and Saunders M A (2005a) Users' guide for SQOPT 7: a Fortran package for largescale linear and quadratic programming
Report NA 051 Department of Mathematics, University of California, San Diego
http://www.ccom.ucsd.edu/~peg/papers/sqdoc7.pdf
Gill P E, Murray W and Saunders M A (2005b) Users' guide for SNOPT 7.1: a Fortran package for largescale linear nonlinear programming
Report NA 052 Department of Mathematics, University of California, San Diego
http://www.ccom.ucsd.edu/~peg/papers/sndoc7.pdf
Gill P E, Murray W, Saunders M A and Wright M H (1987) Maintaining LU factors of a general sparse matrix Linear Algebra and its Applics. 88/89 239–270
Gill P E, Murray W, Saunders M A and Wright M H (1989) A practical anticycling procedure for linearly constrained optimization Math. Programming 45 437–474
Gill P E, Murray W, Saunders M A and Wright M H (1991) Inertiacontrolling methods for general quadratic programming SIAM Rev. 33 1–36
Hall J A J and McKinnon K I M (1996) The simplest examples where the simplex method cycles and conditions where EXPAND fails to prevent cycling Report MS 96–100 Department of Mathematics and Statistics, University of Edinburgh
5 Arguments
The first
$n$ entries of the arguments
bl,
bu,
hs and
x refer to the variables
$x$. The last
$m$ entries refer to the slacks
$s$.
 1:
start – Nag_StartInput
On entry: indicates how a starting basis (and certain other items) will be obtained.
 ${\mathbf{start}}=\mathrm{Nag\_Cold}$
 Requests that an internal Crash procedure be used to choose an initial basis, unless a Basis file is provided via optional arguments ${\mathbf{Old\; Basis\; File}}$, ${\mathbf{Insert\; File}}$ or ${\mathbf{Load\; File}}$.
 ${\mathbf{start}}=\mathrm{Nag\_BasisFile}$
 Is the same as ${\mathbf{start}}=\mathrm{Nag\_Cold}$ but is more meaningful when a Basis file is given.
 ${\mathbf{start}}=\mathrm{Nag\_Warm}$
 Means that a basis is already defined in hs and a start point is already defined in x (probably from an earlier call).
Constraint:
${\mathbf{start}}=\mathrm{Nag\_BasisFile}$, $\mathrm{Nag\_Cold}$ or $\mathrm{Nag\_Warm}$.
 2:
qphx – function, supplied by the userExternal Function
For QP problems, you must supply a version of
qphx to compute the matrix product
$Hx$ for a given vector
$x$. If
$H$ has rows and columns of zeros, it is most efficient to order
$x$ so that the nonlinear variables appear first. For example, if
$x={\left(y,z\right)}^{\mathrm{T}}$ and only
$y$ enters the objective quadratically then
In this case,
ncolh should be the dimension of
$y$, and
qphx should compute
${H}_{1}y$. For FP and LP problems,
qphx will never be called by nag_opt_sparse_convex_qp_solve (e04nqc) and hence
qphx may be specified as
NULLFN.
The specification of
qphx is:
void 
qphx (Integer ncolh,
const double x[],
double hx[],
Integer nstate)


 1:
ncolh – IntegerInput
On entry: this is the same argument
ncolh as supplied to nag_opt_sparse_convex_qp_solve (e04nqc).
 2:
x[ncolh] – const doubleInput
On entry: the first
ncolh elements of the vector
$x$.
 3:
hx[ncolh] – doubleOutput
On exit: the product
$Hx$. If
ncolh is less than the input argument
n,
$Hx$ is really the product
${H}_{1}y$ in
(2).
 4:
nstate – IntegerInput
On entry: allows you to save computation time if certain data must be read or calculated only once. To preserve this data for a subsequent calculation place it in
comm.
 ${\mathbf{nstate}}=1$
 nag_opt_sparse_convex_qp_solve (e04nqc) is calling qphx for the first time.
 ${\mathbf{nstate}}=0$
 There is nothing special about the current call of qphx.
 ${\mathbf{nstate}}\ge 2$
 nag_opt_sparse_convex_qp_solve (e04nqc) is calling qphx for the last time. This argument setting allows you to perform some additional computation on the final solution.
 ${\mathbf{nstate}}=2$
 The current $x$ is optimal.
 ${\mathbf{nstate}}=3$
 The problem appears to be infeasible.
 ${\mathbf{nstate}}=4$
 The problem appears to be unbounded.
 ${\mathbf{nstate}}=5$
 The iterations limit was reached.
 5:
comm – Nag_Comm *
Pointer to structure of type Nag_Comm; the following members are relevant to
qphx.
 user – double *
 iuser – Integer *
 p – Pointer
The type Pointer will be
void *. Before calling nag_opt_sparse_convex_qp_solve (e04nqc) you may allocate memory and initialize these pointers with various quantities for use by
qphx when called from nag_opt_sparse_convex_qp_solve (e04nqc) (see
Section 3.2.1.1 in the Essential Introduction).
 3:
m – IntegerInput
On entry:
$m$, the number of general linear constraints (or slacks). This is the number of rows in the linear constraint matrix
$A$, including the free row (if any; see
iobj). Note that
$A$ must have at least one row. If your problem has no constraints, or only upper or lower bounds on the variables, then you must include a dummy row with sufficiently wide upper and lower bounds (see also
acol,
inda and
loca).
Constraint:
${\mathbf{m}}\ge 1$.
 4:
n – IntegerInput
On entry: $n$, the number of variables (excluding slacks). This is the number of columns in the linear constraint matrix $A$.
Constraint:
${\mathbf{n}}\ge 1$.
 5:
ne – IntegerInput
On entry: the number of nonzero elements in $A$.
Constraint:
$1\le {\mathbf{ne}}\le {\mathbf{n}}\times {\mathbf{m}}$.
 6:
nname – IntegerInput
On entry: the number of column (i.e., variable) and row names supplied in the array
names.
 ${\mathbf{nname}}=1$
 There are no names. Default names will be used in the printed output.
 ${\mathbf{nname}}={\mathbf{n}}+{\mathbf{m}}$
 All names must be supplied.
Constraint:
${\mathbf{nname}}=1$ or ${\mathbf{n}}+{\mathbf{m}}$.
 7:
lenc – IntegerInput
On entry: the number of elements in the constant objective vector
$c$.
If
${\mathbf{lenc}}>0$, the first
lenc elements of
$x$ belong to variables corresponding to the constant objective term
$c$.
Constraint:
$0\le {\mathbf{lenc}}\le {\mathbf{n}}$.
 8:
ncolh – IntegerInput
On entry:
${n}_{H}$, the number of leading nonzero columns of the Hessian matrix
$H$. For FP and LP problems,
ncolh must be set to zero.
The first
ncolh elements of
$x$ belong to variables corresponding to the nonzero block of the QP Hessian.
Constraint:
$0\le {\mathbf{ncolh}}\le {\mathbf{n}}$.
 9:
iobj – IntegerInput
On entry: if
${\mathbf{iobj}}>0$, row
iobj of
$A$ is a free row containing the nonzero elements of the vector
$c$ appearing in the linear objective term
${c}^{\mathrm{T}}x$.
If
${\mathbf{iobj}}=0$, there is no free row, and the linear objective vector should be supplied in array
c.
Constraint:
$0\le {\mathbf{iobj}}\le {\mathbf{m}}$.
 10:
objadd – doubleInput
On entry: the constant $q$, to be added to the objective for printing purposes. Typically ${\mathbf{objadd}}=0.0$.
 11:
prob – const char *Input
On entry: the name for the problem. It is used in the printed solution and in some functions that output Basis files. Only the first eight characters of
prob are significant.
 12:
acol[ne] – const doubleInput
On entry: the nonzero elements of $A$, ordered by increasing column index. Note that all elements must be assigned a value in the calling program.
 13:
inda[ne] – const IntegerInput
On entry:
${\mathbf{inda}}\left[\mathit{i}1\right]$ must contain the row index of the nonzero element stored in
${\mathbf{acol}}\left[\mathit{i}1\right]$, for
$\mathit{i}=1,2,\dots ,{\mathbf{ne}}$. Thus a pair of values
$\left({\mathbf{acol}}\left[i1\right],{\mathbf{inda}}\left[i1\right]\right)$ contains a matrix element and its corresponding row index.
If
${\mathbf{lenc}}>0$, the first
lenc elements of
acol and
inda belong to variables corresponding to the constant objectiver term
$c$.
If the problem has a quadratic objective, the first
ncolh columns of
acol and
inda belong to variables corresponding to the nonzero block of the
$QP$ Hessian. Function
qphx knows about these variables.
Note that the row indices for a column must lie in the range
$1$ to
m, and may be supplied in any order.
Constraint:
$1\le {\mathbf{inda}}\left[\mathit{i}1\right]\le {\mathbf{m}}$, for $\mathit{i}=1,2,\dots ,{\mathbf{ne}}$.
 14:
loca[${\mathbf{n}}+1$] – const IntegerInput
On entry:
${\mathbf{loca}}\left[\mathit{j}1\right]$ must contain the value
$p+1$, where
$p$ is the index in
acol and
inda of the start of the
$\mathit{j}$th column, for
$\mathit{j}=1,2,\dots ,{\mathbf{n}}$. Thus, the entries of column
$j$ are held in
${\mathbf{acol}}\left[i1\right]$, and their corresponding row indices are in
${\mathbf{inda}}\left[\mathit{i}1\right]$, for
$\mathit{i}=k,\dots ,l$, where
$k={\mathbf{loca}}\left[j1\right]$ and
$l={\mathbf{loca}}\left[j\right]1$. To specify the
$j$th column as empty, set
${\mathbf{loca}}\left[j1\right]={\mathbf{loca}}\left[j\right]$. Note that the first and last elements of
loca must be
${\mathbf{loca}}\left[0\right]=1$ and
${\mathbf{loca}}\left[{\mathbf{n}}\right]={\mathbf{ne}}+1$. If your problem has no constraints, or just bounds on the variables, you may include a dummy ‘free’ row with a single (zero) element by setting
${\mathbf{ne}}=1$,
${\mathbf{acol}}\left[0\right]=0.0$,
${\mathbf{inda}}\left[0\right]=1$,
${\mathbf{loca}}\left[0\right]=1$, and
${\mathbf{loca}}\left[j1\right]=2$, for
$j=1,2\dots ,{\mathbf{n}}$. This row is made ‘free’ by setting its bounds to be
${\mathbf{bl}}\left[{\mathbf{n}}\right]=\mathit{bigbnd}$ and
${\mathbf{bu}}\left[{\mathbf{n}}\right]=\mathit{bigbnd}$, where
$\mathit{bigbnd}$ is the value of the optional argument
${\mathbf{Infinite\; Bound\; Size}}$.
Constraints:
 ${\mathbf{loca}}\left[0\right]=1$;
 ${\mathbf{loca}}\left[\mathit{j}1\right]\ge 1$, for $\mathit{j}=2,3,\dots ,{\mathbf{n}}$;
 ${\mathbf{loca}}\left[{\mathbf{n}}\right]={\mathbf{ne}}+1$;
 $0\le {\mathbf{loca}}\left[\mathit{j}\right]{\mathbf{loca}}\left[\mathit{j}1\right]\le {\mathbf{m}}$, for $\mathit{j}=1,2,\dots ,{\mathbf{n}}$.
 15:
bl[${\mathbf{n}}+{\mathbf{m}}$] – const doubleInput
On entry:
$l$, the lower bounds for all the variables and general constraints, in the following order. The first
n elements of
bl must contain the bounds on the variables
$x$, and the next
m elements the bounds for the general linear constraints
$Ax$ (which, equivalently, are the bounds for the slacks,
$s$) and the free row (if any). To fix the
$j$th variable, set
${\mathbf{bl}}\left[j1\right]={\mathbf{bu}}\left[j1\right]=\beta $, say, where
$\left\beta \right<\mathit{bigbnd}$. To specify a nonexistent lower bound (i.e.,
${l}_{j}=\infty $), set
${\mathbf{bl}}\left[j1\right]\le \mathit{bigbnd}$. Here,
$\mathit{bigbnd}$ is the value of the optional argument
${\mathbf{Infinite\; Bound\; Size}}$. To specify the
$j$th constraint as an
equality, set
${\mathbf{bl}}\left[{\mathbf{n}}+j1\right]={\mathbf{bu}}\left[{\mathbf{n}}+j1\right]=\beta $, say, where
$\left\beta \right<\mathit{bigbnd}$. Note that the lower bound corresponding to the free row must be set to
$\infty $ and stored in
${\mathbf{bl}}\left[{\mathbf{n}}+{\mathbf{iobj}}1\right]$.
Constraint:
if
${\mathbf{iobj}}>0$,
${\mathbf{bl}}\left[{\mathbf{n}}+{\mathbf{iobj}}1\right]\le \mathit{bigbnd}$(See also the description for
bu.)
 16:
bu[${\mathbf{n}}+{\mathbf{m}}$] – const doubleInput
On entry:
$u$, the upper bounds for all the variables and general constraints, in the following order. The first
n elements of
bu must contain the bounds on the variables
$x$, and the next
m elements the bounds for the general linear constraints
$Ax$ (which, equivalently, are the bounds for the slacks,
$s$) and the free row (if any). To specify a nonexistent upper bound (i.e.,
${u}_{j}=+\infty $), set
${\mathbf{bu}}\left[j1\right]\ge \mathit{bigbnd}$. Note that the upper bound corresponding to the free row must be set to
$+\infty $ and stored in
${\mathbf{bu}}\left[{\mathbf{n}}+{\mathbf{iobj}}1\right]$.
Constraints:
 if ${\mathbf{iobj}}>0$, ${\mathbf{bu}}\left[{\mathbf{n}}+{\mathbf{iobj}}1\right]\ge \mathit{bigbnd}$;
 otherwise ${\mathbf{bl}}\left[i1\right]\le {\mathbf{bu}}\left[i1\right]$.
 17:
c[lenc] – const doubleInput
On entry: contains the explicit objective vector
$c$ (if any). If
${\mathbf{lenc}}=0$, then
c is not referenced and may be
NULL.
 18:
names[nname] – const char *Input
On entry: the optional column and row names, respectively.
If
${\mathbf{nname}}=1$,
names is not referenced and the printed output will use default names for the columns and rows.
If
${\mathbf{nname}}={\mathbf{n}}+{\mathbf{m}}$, the first
n elements must contain the names for the columns and the next
m elements must contain the names for the rows. Note that the name for the free row (if any) must be stored in
${\mathbf{names}}\left[{\mathbf{n}}+{\mathbf{iobj}}1\right]$.
Note: that only the first eight characters of the strings in
names are significant.
 19:
helast[${\mathbf{n}}+{\mathbf{m}}$] – const IntegerInput
On entry: defines which variables are to be treated as being elastic in elastic mode. The allowed values of
helast are:
${\mathbf{helast}}\left[j1\right]$  Status in elastic mode 
$0$  Variable $j$ is nonelastic and cannot be infeasible 
$1$  Variable $j$ can violate its lower bound 
$2$  Variable $j$ can violate its upper bound 
$3$  Variable $j$ can violate either its lower or upper bound 
helast need not be assigned if optional argument
${\mathbf{Elastic\; Mode}}=0$.
Constraint:
if ${\mathbf{Elastic\; Mode}}\ne 0$, ${\mathbf{helast}}\left[\mathit{j}1\right]=0,1,2,3$, for $\mathit{j}=1,2,\dots ,{\mathbf{n}}+{\mathbf{m}}$.
 20:
hs[${\mathbf{n}}+{\mathbf{m}}$] – IntegerInput/Output
On entry: if
${\mathbf{start}}=\mathrm{Nag\_Cold}$ or
$\mathrm{Nag\_BasisFile}$, and a Basis file of some sort is to be input (see the description of the optional arguments
${\mathbf{Old\; Basis\; File}}$,
${\mathbf{Insert\; File}}$ or
${\mathbf{Load\; File}}$), then
hs and
x need not be set at all.
If
${\mathbf{start}}=\mathrm{Nag\_Cold}$ or
$\mathrm{Nag\_BasisFile}$ and there is no Basis file, the first
n elements of
hs and
x must specify the initial states and values, respectively, of the variables
$x$. (The slacks
$s$ need not be initialized.) An internal Crash procedure is then used to select an initial basis matrix
$B$. The initial basis matrix will be triangular (neglecting certain small elements in each column). It is chosen from various rows and columns of
$\left(\begin{array}{cc}A& I\end{array}\right)$. Possible values for
${\mathbf{hs}}\left[j1\right]$ are as follows:
${\mathbf{hs}}\left[j1\right]$  State of ${\mathbf{x}}\left[j1\right]$ during Crash procedure 
$0$ or $1$  Eligible for the basis 
$2$  Ignored 
$3$  Eligible for the basis (given preference over $0$ or $1$) 
$4$ or $5$  Ignored 
If nothing special is known about the problem, or there is no wish to provide special information, you may set
${\mathbf{hs}}\left[\mathit{j}1\right]=0$ and ${\mathbf{x}}\left[\mathit{j}1\right]=0.0$, for $\mathit{j}=1,2,\dots ,{\mathbf{n}}$. All variables will then be eligible for the initial basis. Less trivially, to say that the $j$th variable will probably be equal to one of its bounds, set ${\mathbf{hs}}\left[j1\right]=4$ and ${\mathbf{x}}\left[j1\right]={\mathbf{bl}}\left[j1\right]$ or ${\mathbf{hs}}\left[j1\right]=5$ and ${\mathbf{x}}\left[j1\right]={\mathbf{bu}}\left[j1\right]$ as appropriate.
Following the Crash procedure, variables for which ${\mathbf{hs}}\left[j1\right]=2$ are made superbasic. Other variables not selected for the basis are then made nonbasic at the value ${\mathbf{x}}\left[j1\right]$ if ${\mathbf{bl}}\left[j1\right]\le {\mathbf{x}}\left[j1\right]\le {\mathbf{bu}}\left[j1\right]$, or at the value ${\mathbf{bl}}\left[j1\right]$ or ${\mathbf{bu}}\left[j1\right]$ closest to ${\mathbf{x}}\left[j1\right]$.
If
${\mathbf{start}}=\mathrm{Nag\_Warm}$,
hs and
x must specify the initial states and values, respectively, of the variables and slacks
$\left(x,s\right)$. If nag_opt_sparse_convex_qp_solve (e04nqc) has been called previously with the same values of
n and
m,
hs already contains satisfactory information.
Constraints:
 if ${\mathbf{start}}=\mathrm{Nag\_Cold}$ or $\mathrm{Nag\_BasisFile}$, $0\le {\mathbf{hs}}\left[\mathit{j}1\right]\le 5$, for $\mathit{j}=1,2,\dots ,{\mathbf{n}}$;
 if ${\mathbf{start}}=\mathrm{Nag\_Warm}$, $0\le {\mathbf{hs}}\left[\mathit{j}1\right]\le 3$, for $\mathit{j}=1,2,\dots ,{\mathbf{n}}+{\mathbf{m}}$.
On exit: the final states of the variables and slacks
$\left(x,s\right)$. The significance of each possible value of
${\mathbf{hs}}\left[j1\right]$ is as follows:
${\mathbf{hs}}\left[j1\right]$  State of variable $j$  Normal value of ${\mathbf{x}}\left[j1\right]$ 
$0$  Nonbasic  ${\mathbf{bl}}\left[j1\right]$ 
$1$  Nonbasic  ${\mathbf{bu}}\left[j1\right]$ 
$2$  Superbasic  Between ${\mathbf{bl}}\left[j1\right]$ and ${\mathbf{bu}}\left[j1\right]$ 
$3$  Basic  Between ${\mathbf{bl}}\left[j1\right]$ and ${\mathbf{bu}}\left[j1\right]$ 
If ${\mathbf{ninf}}=0$, basic and superbasic variables may be outside their bounds by as much as the value of the optional argument ${\mathbf{Feasibility\; Tolerance}}$. Note that unless the optional argument ${\mathbf{Scale\; Option}}=0$ is specified, the optional argument ${\mathbf{Feasibility\; Tolerance}}$ applies to the variables of the scaled problem. In this case, the variables of the original problem may be as much as $0.1$ outside their bounds, but this is unlikely unless the problem is very badly scaled.
Very occasionally some nonbasic variables may be outside their bounds by as much as the optional argument ${\mathbf{Feasibility\; Tolerance}}$, and there may be some nonbasic variables for which ${\mathbf{x}}\left[j1\right]$ lies strictly between its bounds.
If
${\mathbf{ninf}}>0$, some basic and superbasic variables may be outside their bounds by an arbitrary amount (bounded by
sinf if
${\mathbf{Scale\; Option}}=0$).
 21:
x[${\mathbf{n}}+{\mathbf{m}}$] – doubleInput/Output
On entry: the initial values of the variables
$x$, and, if
${\mathbf{start}}=\mathrm{Nag\_Warm}$, the slacks
$s$, i.e.,
$\left(x,s\right)$. (See the description for argument
hs.)
On exit: the final values of the variables and slacks $\left(x,s\right)$.
 22:
pi[m] – doubleOutput
On exit: contains the dual variables $\pi $ (a set of Lagrange multipliers (shadow prices) for the general constraints).
 23:
rc[${\mathbf{n}}+{\mathbf{m}}$] – doubleOutput
On exit: contains the reduced costs,
$g{\left(\begin{array}{cc}A& I\end{array}\right)}^{\mathrm{T}}\pi $. The vector
$g$ is the gradient of the objective if
x is feasible, otherwise it is the gradient of the Phase 1 objective. In the former case,
$g\left(i\right)=0$, for
$i={\mathbf{n}}+1:{\mathbf{m}}$, hence
${\mathbf{rc}}\left[{\mathbf{n}}+1:{\mathbf{m}}1\right]=\pi $.
 24:
ns – Integer *Input/Output
On entry:
${n}_{S}$, the number of superbasics. For QP problems,
ns need not be specified if
${\mathbf{start}}=\mathrm{Nag\_Cold}$, but must retain its value from a previous call when
${\mathbf{start}}=\mathrm{Nag\_Warm}$. For FP and LP problems,
ns need not be initialized.
On exit: the final number of superbasics. This will be zero for FP and LP problems.
 25:
ninf – Integer *Output
On exit: the number of infeasibilities.
 26:
sinf – double *Output
On exit: the sum of the scaled infeasibilities. This will be zero if ${\mathbf{ninf}}=0$, and is most meaningful when ${\mathbf{Scale\; Option}}=0$.
 27:
obj – double *Output
On exit: the value of the objective function.
If
${\mathbf{ninf}}=0$,
obj includes the quadratic objective term
$\frac{1}{2}{x}^{\mathrm{T}}Hx$ (if any).
If
${\mathbf{ninf}}>0$,
obj is just the linear objective term
${c}^{\mathrm{T}}x$ (if any).
For FP problems,
obj is set to zero.
Note that
obj does not include contributions from the constant term
objadd or the objective row, if any.
 28:
state – Nag_E04State *Communication Structure
state contains internal information required for functions in this suite. It must not be modified in any way.
 29:
comm – Nag_Comm *Communication Structure

The NAG communication argument (see
Section 3.2.1.1 in the Essential Introduction).
 30:
fail – NagError *Input/Output

The NAG error argument (see
Section 3.6 in the Essential Introduction).
nag_opt_sparse_convex_qp_solve (e04nqc) returns with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR if the reduced gradient (
rgNorm; see
Section 9.1) is negligible, the Lagrange multipliers (
Lagr Mult; see
Section 9.1) are optimal,
$x$ satisfies the constraints to the accuracy requested by the value of the optional argument
${\mathbf{Feasibility\; Tolerance}}$ and the reduced Hessian factor
$R$ (see
Section 11.2) is nonsingular.
6 Error Indicators and Warnings
 NE_ALLOC_FAIL

Dynamic memory allocation failed.
Internal memory allocation failed when attempting to obtain workspace sizes
$\u27e8\mathit{\text{value}}\u27e9$,
$\u27e8\mathit{\text{value}}\u27e9$ and
$\u27e8\mathit{\text{value}}\u27e9$. Please contact
NAG.
 NE_ALLOC_INSUFFICIENT

Internal memory allocation was insufficient. Please contact
NAG.
 NE_ARRAY_INPUT

On entry, ${\mathbf{loca}}\left[0\right]=\u27e8\mathit{\text{value}}\u27e9$, ${\mathbf{loca}}\left[\u27e8\mathit{\text{value}}\u27e9\right]=\u27e8\mathit{\text{value}}\u27e9$, ${\mathbf{ne}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{loca}}\left[0\right]=1$ or ${\mathbf{loca}}\left[\u27e8\mathit{\text{value}}\u27e9\right]={\mathbf{ne}}+1$.
On entry, row index $\u27e8\mathit{\text{value}}\u27e9$ in ${\mathbf{inda}}\left[\u27e8\mathit{\text{value}}\u27e9\right]$ is outside the range $1$ to ${\mathbf{m}}=\u27e8\mathit{\text{value}}\u27e9$.
 NE_BAD_PARAM

Basis file dimensions do not match this problem.
On entry, argument $\u27e8\mathit{\text{value}}\u27e9$ had an illegal value.
 NE_BASIS_FAILURE

An error has occurred in the basis package, perhaps indicating incorrect setup of arrays
inda and
loca. Set the optional argument
${\mathbf{Print\; File}}$ and examine the output carefully for further information.
 NE_BASIS_ILL_COND

Numerical difficulties have been encountered and no further progress can be made.
 NE_BASIS_SINGULAR

The basis is singular after several attempts to factorize it (and add slacks where necessary).
 NE_E04NPC_NOT_INIT

Initialization function
nag_opt_sparse_convex_qp_init (e04npc) has not been called.
 NE_HESS_INDEF

Error in
qphx: the QP Hessian is indefinite.
 NE_HESS_TOO_BIG

The value of the optional argument ${\mathbf{Superbasics\; Limit}}$ is too small.
 NE_INT

On entry, ${\mathbf{m}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{m}}\ge 1$.
On entry, ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{n}}\ge 1$.
 NE_INT_2

On entry, ${\mathbf{iobj}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{m}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: $0\le {\mathbf{iobj}}\le {\mathbf{m}}$.
On entry, ${\mathbf{lenc}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: $0\le {\mathbf{lenc}}\le {\mathbf{n}}$.
On entry, ${\mathbf{ncolh}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: $0\le {\mathbf{ncolh}}\le {\mathbf{n}}$.
On entry,
ne is not equal to the number of nonzeros in
acol.
${\mathbf{ne}}=\u27e8\mathit{\text{value}}\u27e9$, nonzeros in
${\mathbf{acol}}=\u27e8\mathit{\text{value}}\u27e9$.
 NE_INT_3

On entry, ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$, ${\mathbf{m}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{ne}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: $1\le {\mathbf{ne}}\le {\mathbf{n}}\times {\mathbf{m}}$.
On entry, ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$, ${\mathbf{m}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{nname}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{nname}}=1$ or ${\mathbf{n}}+{\mathbf{m}}$.
On entry, ${\mathbf{ne}}=\u27e8\mathit{\text{value}}\u27e9$, ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{m}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: $1\le {\mathbf{ne}}\le {\mathbf{n}}\times {\mathbf{m}}$.
On entry, ${\mathbf{nname}}=\u27e8\mathit{\text{value}}\u27e9$, ${\mathbf{n}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{m}}=\u27e8\mathit{\text{value}}\u27e9$.
Constraint: ${\mathbf{nname}}=1$ or ${\mathbf{n}}+{\mathbf{m}}$.
 NE_INTERNAL_ERROR

An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact
NAG for assistance.
An unexpected error has occurred. Set the optional argument ${\mathbf{Print\; File}}$ and examine the output carefully for further information.
 NE_NOT_REQUIRED_ACC

The requested accuracy could not be achieved.
 NE_REAL_2

On entry, bounds
bl and
bu for
$\u27e8\mathit{\text{value}}\u27e9$ are equal and infinite:
${\mathbf{bl}}={\mathbf{bu}}=\u27e8\mathit{\text{value}}\u27e9$ and
$\mathit{infbnd}=\u27e8\mathit{\text{value}}\u27e9$.
On entry, bounds
bl and
bu for
$\u27e8\mathit{\text{value}}\u27e9$ are equal and infinite.
${\mathbf{bl}}={\mathbf{bu}}=\u27e8\mathit{\text{value}}\u27e9$ and
$\mathit{infbnd}=\u27e8\mathit{\text{value}}\u27e9$.
On entry, bounds for $\u27e8\mathit{\text{value}}\u27e9$ are inconsistent. ${\mathbf{bl}}=\u27e8\mathit{\text{value}}\u27e9$ and ${\mathbf{bu}}=\u27e8\mathit{\text{value}}\u27e9$.
 NE_UNBOUNDED

The problem appears to be unbounded. The constraint violation limit has been reached.
The problem appears to be unbounded. The objective function is unbounded.
 NW_NOT_FEASIBLE

The linear constraints appear to be infeasible.
The problem appears to be infeasible. Infeasibilites have been minimized.
The problem appears to be infeasible. Nonlinear infeasibilites have been minimized.
The problem appears to be infeasible. The linear equality constraints could not be satisfied.
 NW_SOLN_NOT_UNIQUE

Weak solution found – the solution is not unique.
 NW_TOO_MANY_ITER

Iteration limit reached.
Major iteration limit reached.
7 Accuracy
nag_opt_sparse_convex_qp_solve (e04nqc) implements a numerically stable activeset strategy and returns solutions that are as accurate as the condition of the problem warrants on the machine.
8 Parallelism and Performance
nag_opt_sparse_convex_qp_solve (e04nqc) is not threaded by NAG in any implementation.
nag_opt_sparse_convex_qp_solve (e04nqc) makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the
Users' Note for your implementation for any additional implementationspecific information.
This section contains a description of the printed output.
9.1 Description of the Printed Output
If ${\mathbf{Print\; Level}}>0$, one line of information is output to the ${\mathbf{Print\; File}}$ every $k$th iteration, where $k$ is the specified ${\mathbf{Print\; Frequency}}$. A heading is printed before the first such line following a basis factorization. The heading contains the items described below. In this description, a pricing operation is defined to be the process by which one or more nonbasic variables are selected to become superbasic (in addition to those already in the superbasic set). The variable selected will be denoted by jq. If the problem is purely linear, variable jq will usually become basic immediately (unless it should happen to reach its opposite bound and return to the nonbasic set).
If optional argument
${\mathbf{Partial\; Price}}$ is in effect, variable
jq is selected from
${A}_{\mathtt{pp}}$ or
${I}_{\mathtt{pp}}$, the
ppth segments of the constraint matrix
$\left(\begin{array}{cc}A& I\end{array}\right)$.
Label 
Description 
Itn 
is the iteration count.

pp 
is the partialprice indicator. The variable selected by the last pricing operation came from the ppth partition of $A$ and $I$. Note that pp is reset to zero whenever the basis is refactorized. 
dj 
is the value of the reduced gradient (or reduced cost) for the variable selected by the pricing operation at the start of the current iteration.
Algebraically, dj is ${d}_{j}={g}_{j}{\pi}^{\mathrm{T}}{a}_{j}$, for $j=\mathtt{jq}$, where ${g}_{j}$ is the gradient of the current objective function, $\pi $ is the vector of dual variables, and ${a}_{j}$ is the $j$th column of the constraint matrix $\left(\begin{array}{cc}A& I\end{array}\right)$.
Note that dj is the norm of the reducedgradient vector at the start of the iteration, just after the pricing operation. 
+SBS 
is the variable jq selected by the pricing operation to be added to the superbasic set. 
SBS 
is the variable chosen to leave the superbasic set. It has become basic if the entry under B is nonzero, otherwise it becomes nonbasic. 
BS 
is the variable removed from the basis to become nonbasic. 
Step 
is the value of the step length $\alpha $ taken along the current search direction $p$. The variables $x$ have just been changed to $x+\alpha p$. If a variable is made superbasic during the current iteration (i.e., +SBS is positive), Step will be the step to the nearest bound. During the optimality phase, the step can be greater than unity only if the reduced Hessian is not positive definite. 
Pivot 
is the $r$th element of a vector $y$ satisfying $By={a}_{q}$ whenever ${a}_{q}$ (the $q$th column of the constraint matrix $\left(\begin{array}{cc}A& I\end{array}\right)$ replaces the $r$th column of the basis matrix $B$. Wherever possible, Step is chosen so as to avoid extremely small values of Pivot (since they may cause the basis to be nearly singular). In extreme cases, it may be necessary to increase the value of the optional argument ${\mathbf{Pivot\; Tolerance}}$ to exclude very small elements of $y$ from consideration during the computation of Step. 
nInf 
is the number of violated constraints (infeasibilities) before the present iteration. This number will not increase unless iterations are in elastic mode.

sInf 
is the sum of infeasibilities before the present iteration. It will usually decrease at each nonzero step, but if nInf decreases by $2$ or more, sInf may occasionally increase. However, in elastic mode it will decrease monotonically.

Objective 
is the value of the current objective function after the present iteration. Note, if ${\mathbf{Elastic\; Mode}}$ is $2$, the heading is Composite Obj.

L+U 
L is the number of nonzeros in the basis factor $L$. Immediately after a basis factorization $B=LU$, L contains lenL (see Section 13). Further nonzeros are added to L when various columns of $B$ are later replaced. (Thus, L increases monotonically.) U is the number of nonzeros in the basis factor $U$. Immediately after a basis factorization $B=LU$, U contains lenU (see Section 13). As columns of $B$ are replaced, the matrix $U$ is maintained explicitly (in sparse form). The value of U may fluctuate up or down; in general, it will tend to increase. 
ncp 
is the number of compressions required to recover workspace in the data structure for $U$. This includes the number of compressions needed during the previous basis factorization. Normally, ncp should increase very slowly. 
The following will be output if the problem is QP or if the superbasic set is nonempty.
Label 
Description 
rgNorm 
is the largest reducedgradient among the superbasic variables after the current iteration. During the optimality phase, this will be approximately zero after a unit step. 
nS 
is the current number of superbasic variables. 
condHz 
is a lower bound on the condition number of the reduced Hessian (see Section 11.2). The larger this number, the more difficult the problem. Attention should be given to the scaling of the variables and the constraints to guard against high values of condHz. 
10 Example
This example minimizes the quadratic function
$f\left(x\right)={c}^{\mathrm{T}}x+\frac{1}{2}{x}^{\mathrm{T}}Hx$, where
subject to the bounds
and to the linear constraints
The initial point, which is infeasible, is
The optimal solution (to five figures) is
One bound constraint and four linear constraints are active at the solution. Note that the Hessian matrix
$H$ is positive semidefinite.
10.1 Program Text
Program Text (e04nqce.c)
10.2 Program Data
Program Data (e04nqce.d)
10.3 Program Results
Program Results (e04nqce.r)
Note: the remainder of this document is intended for more advanced users. Section 11 contains a detailed description of the algorithm which may be needed in order to understand Sections 12 and 13. Section 12 describes the optional arguments which may be set by calls to nag_opt_sparse_convex_qp_option_set_file (e04nrc), nag_opt_sparse_convex_qp_option_set_string (e04nsc), nag_opt_sparse_convex_qp_option_set_integer (e04ntc) and/or nag_opt_sparse_convex_qp_option_set_double (e04nuc). Section 13 describes the quantities which can be requested to monitor the course of the computation.
11 Algorithmic Details
This section contains a detailed description of the method used by nag_opt_sparse_convex_qp_solve (e04nqc).
11.1 Overview
nag_opt_sparse_convex_qp_solve (e04nqc) is based on an inertiacontrolling method that maintains a Cholesky factorization of the reduced Hessian (see below). The method is similar to that of
Gill and Murray (1978), and is described in detail by
Gill et al. (1991). Here we briefly summarise the main features of the method. Where possible, explicit reference is made to the names of variables that are arguments of the function or appear in the printed output.
The method used has two distinct phases: finding an initial feasible point by minimizing the sum of infeasibilities (the
feasibility phase), and minimizing the quadratic objective function within the feasible region (the
optimality phase). The computations in both phases are performed by the same functions. The twophase nature of the algorithm is reflected by changing the function being minimized from the sum of infeasibilities (the printed quantity
sInf; see
Section 9.1) to the quadratic objective function (the printed quantity
Objective; see
Section 9.1).
In general, an iterative process is required to solve a quadratic program. Given an iterate
$\left(x,s\right)$ in both the original variables
$x$ and the slack variables
$s$, a new iterate
$\left(\stackrel{}{x},\stackrel{}{s}\right)$ is defined by
where the
step length
$\alpha $ is a nonnegative scalar (the printed quantity
Step; see
Section 13), and
$p$ is called the
search direction. (For simplicity, we shall consider a typical iteration and avoid reference to the index of the iteration.) Once an iterate is feasible (i.e., satisfies the constraints), all subsequent iterates remain feasible.
11.2 Definition of the Working Set and Search Direction
At each iterate $\left(x,s\right)$, a working set of constraints is defined to be a linearly independent subset of the constraints that are satisfied ‘exactly’ (to within the value of the optional argument ${\mathbf{Feasibility\; Tolerance}}$). The working set is the current prediction of the constraints that hold with equality at a solution of the LP or QP problem. Let ${m}_{W}$ denote the number of constraints in the working set (including bounds), and let $W$ denote the associated ${m}_{W}$ by $\left(n+m\right)$ working set matrix consisting of the ${m}_{W}$ gradients of the working set constraints.
The search direction is defined so that constraints in the working set remain
unaltered for any value of the step length. It follows that
$p$ must satisfy the identity
This characterisation allows
$p$ to be computed using any
$n$ by
${n}_{Z}$ fullrank matrix
$Z$ that spans the null space of
$W$. (Thus,
${n}_{Z}=n{m}_{W}$ and
$WZ=0$.) The null space matrix
$Z$ is defined from a sparse
$LU$ factorization of part of
$W$ (see
(7) and
(8)). The direction
$p$ will satisfy
(4) if
where
${p}_{Z}$ is any
${n}_{Z}$vector.
The working set contains the constraints $Axs=0$ and a subset of the upper and lower bounds on the variables $\left(x,s\right)$. Since the gradient of a bound constraint ${x}_{j}\ge {l}_{j}$ or ${x}_{j}\le {u}_{j}$ is a vector of all zeros except for $\pm 1$ in position $j$, it follows that the working set matrix contains the rows of $\left(\begin{array}{cc}A& I\end{array}\right)$ and the unit rows associated with the upper and lower bounds in the working set.
The working set matrix
$W$ can be represented in terms of a certain column partition of the matrix
$\left(\begin{array}{cc}A& I\end{array}\right)$ by (conceptually) partitioning the constraints
$Axs=0$ so that
where
$B$ is a square nonsingular basis and
${x}_{B}$,
${x}_{S}$ and
${x}_{N}$ are the basic, superbasic and nonbasic variables respectively. The nonbasic variables are equal to their upper or lower bounds at
$\left(x,s\right)$, and the superbasic variables are independent variables that are chosen to improve the value of the current objective function. The number of superbasic variables is
${n}_{S}$ (the printed quantity
nS; see
Section 9.1). Given values of
${x}_{N}$ and
${x}_{S}$, the basic variables
${x}_{B}$ are adjusted so that
$\left(x,s\right)$ satisfies
(6).
If
$P$ is a permutation matrix such that
$\left(\begin{array}{cc}A& I\end{array}\right)P=\left(\begin{array}{ccc}B& S& N\end{array}\right)$, then
$W$ satisfies
where
${I}_{N}$ is the identity matrix with the same number of columns as
$N$.
The null space matrix
$Z$ is defined from a sparse
$LU$ factorization of part of
$W$. In particular,
$Z$ is maintained in ‘reduced gradient’ form, using the LUSOL package (see
Gill et al. (1991)) to maintain sparse
$LU$ factors of the basis matrix
$B$ as the
$BSN$ partition changes. Given the permutation
$P$, the null space basis is given by
This matrix is used only as an operator, i.e., it is never computed explicitly. Products of the form
$Zv$ and
${Z}^{\mathrm{T}}g$ are obtained by solving with
$B$ or
${B}^{\mathrm{T}}$. This choice of
$Z$ implies that
${n}_{Z}$, the number of ‘degrees of freedom’ at
$\left(x,s\right)$, is the same as
${n}_{S}$, the number of superbasic variables.
Let
${g}_{Z}$ and
${H}_{Z}$ denote the
reduced gradient and
reduced Hessian of the objective function:
where
$g$ is the objective gradient at
$\left(x,s\right)$. Roughly speaking,
${g}_{Z}$ and
${H}_{Z}$ describe the first and second derivatives of an
${n}_{S}$dimensional
unconstrained problem for the calculation of
${p}_{Z}$. (The condition estimator of
${H}_{Z}$ is the quantity
condHz in the monitoring file output; see
Section 9.1.)
At each iteration, an upper triangular factor $R$ is available such that ${H}_{Z}={R}^{\mathrm{T}}R$. Normally, $R$ is computed from ${R}^{\mathrm{T}}R={Z}^{\mathrm{T}}HZ$ at the start of the optimality phase and then updated as the QP working set changes. For efficiency, the dimension of $R$ should not be excessive (say, ${n}_{S}\le 1000$). This is guaranteed if the number of nonlinear variables is ‘moderate’.
If the QP problem contains linear variables,
$H$ is positive semidefinite and
$R$ may be singular with at least one zero diagonal element. However, an inertiacontrolling strategy is used to ensure that only the last diagonal element of
$R$ can be zero. (See
Gill et al. (1991) for a discussion of a similar strategy for indefinite quadratic programming.)
If the initial $R$ is singular, enough variables are fixed at their current value to give a nonsingular $R$. This is equivalent to including temporary bound constraints in the working set. Thereafter, $R$ can become singular only when a constraint is deleted from the working set (in which case no further constraints are deleted until $R$ becomes nonsingular).
11.3 Main Iteration
If the reduced gradient is zero,
$\left(x,s\right)$ is a constrained stationary point on the working set. During the feasibility phase, the reduced gradient will usually be zero only at a vertex (although it may be zero elsewhere in the presence of constraint dependencies). During the optimality phase, a zero reduced gradient implies that
$x$ minimizes the quadratic objective function when the constraints in the working set are treated as equalities. At a constrained stationary point, Lagrange multipliers
$\lambda $ are defined from the equations
A Lagrange multiplier,
${\lambda}_{j}$, corresponding to an inequality constraint in the working set is said to be
optimal if
${\lambda}_{j}\le \sigma $ when the associated constraint is at its
upper bound, or if
${\lambda}_{j}\ge \sigma $ when the associated constraint is at its
lower bound, where
$\sigma $ depends on the value of the optional argument
${\mathbf{Optimality\; Tolerance}}$. If a multiplier is nonoptimal, the objective function (either the true objective or the sum of infeasibilities) can be reduced by continuing the minimization with the corresponding constraint excluded from the working set. (This step is sometimes referred to as ‘deleting’ a constraint from the working set.) If optimal multipliers occur during the feasibility phase but the sum of infeasibilities is nonzero, there is no feasible point and the function terminates immediately with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOT_REQUIRED_ACC.
The special form
(7) of the working set allows the multiplier vector
$\lambda $, the solution of
(10), to be written in terms of the vector
where
$\pi $ satisfies the equations
${B}^{\mathrm{T}}\pi ={g}_{B}$, and
${g}_{B}$ denotes the basic elements of
$g$. The elements of
$\pi $ are the Lagrange multipliers
${\lambda}_{j}$ associated with the equality constraints
$Axs=0$. The vector
${d}_{N}$ of nonbasic elements of
$d$ consists of the Lagrange multipliers
${\lambda}_{j}$ associated with the upper and lower bound constraints in the working set. The vector
${d}_{S}$ of superbasic elements of
$d$ is the reduced gradient
${g}_{Z}$ in
(9). The vector
${d}_{B}$ of basic elements of
$d$ is zero, by construction. (The Euclidean norm of
${d}_{S}$ and the final values of
${d}_{S}$,
$g$ and
$\pi $ are the quantities
rgNorm,
Reduced Gradnt,
Obj Gradient and
Dual Activity in the monitoring file output; see
Section 13.)
If the reduced gradient is not zero, Lagrange multipliers need not be computed and the search direction is given by
$p=Z{p}_{Z}$ (see
(8) and
(12)). The step length is chosen to maintain feasibility with respect to the satisfied constraints.
There are two possible choices for
${p}_{Z}$, depending on whether or not
${H}_{Z}$ is singular. If
${H}_{Z}$ is nonsingular,
$R$ is nonsingular and
${p}_{Z}$ in
(5) is computed from the equations
where
${g}_{Z}$ is the reduced gradient at
$x$. In this case,
$\left(x,s\right)+p$ is the minimizer of the objective function subject to the working set constraints being treated as equalities. If
$\left(x,s\right)+p$ is feasible,
$\alpha $ is defined to be unity. In this case, the reduced gradient at
$\left(\stackrel{}{x},\stackrel{}{s}\right)$ will be zero, and Lagrange multipliers are computed at the next iteration. Otherwise,
$\alpha $ is set to
${\alpha}_{N}$, the step to the ‘nearest’ constraint along
$p$. This constraint is then added to the working set at the next iteration.
If
${H}_{Z}$ is singular, then
$R$ must also be singular, and an inertiacontrolling strategy is used to ensure that only the last diagonal element of
$R$ is zero. (See
Gill et al. (1991) for a discussion of a similar strategy for indefinite quadratic programming.) In this case,
${p}_{Z}$ satisfies
which allows the objective function to be reduced by any step of the form
$\left(x,s\right)+\alpha p$, where
$\alpha >0$. The vector
$p=Z{p}_{Z}$ is a direction of unbounded descent for the QP problem in the sense that the QP objective is linear and decreases without bound along
$p$. If no finite step of the form
$\left(x,s\right)+\alpha p$ (where
$\alpha >0$) reaches a constraint not in the working set, the QP problem is unbounded and the function terminates immediately with
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_UNBOUNDED. Otherwise,
$\alpha $ is defined as the maximum feasible step along
$p$ and a constraint active at
$\left(x,s\right)+\alpha p$ is added to the working set for the next iteration.
nag_opt_sparse_convex_qp_solve (e04nqc) makes explicit allowance for infeasible constraints. Infeasible linear constraints are detected first by solving a problem of the form
where
${e}^{\mathrm{T}}=\left(1,1,\dots ,1\right)$. This is equivalent to minimizing the sum of the general linear constraint violations subject to the simple bounds. (In the linear programming literature, the approach is often called
elastic programming.)
11.4 Miscellaneous
If the basis matrix is not chosen carefully, the condition of the null space matrix
$Z$ in
(8) could be arbitrarily high. To guard against this, the function implements a ‘basis repair’ feature in which the LUSOL package (see
Gill et al. (1991)) is used to compute the rectangular factorization
returning just the permutation
$P$ that makes
$PL{P}^{\mathrm{T}}$ unit lower triangular. The pivot tolerance is set to require
${\leftPL{P}^{\mathrm{T}}\right}_{ij}\le 2$, and the permutation is used to define
$P$ in
(7). It can be shown that
$\Vert Z\Vert $ is likely to be little more than unity. Hence,
$Z$ should be wellconditioned
regardless of the condition of
$W$. This feature is applied at the beginning of the optimality phase if a potential
$BS$ ordering is known.
The EXPAND procedure (see
Gill et al. (1989)) is used to reduce the possibility of cycling at a point where the active constraints are nearly linearly dependent. Although there is no absolute guarantee that cycling will not occur, the probability of cycling is extremely small (see
Hall and McKinnon (1996)). The main feature of EXPAND is that the feasibility tolerance is increased at the start of every iteration. This allows a positive step to be taken at every iteration, perhaps at the expense of violating the bounds on
$\left(x,s\right)$ by a small amount.
Suppose that the value of the optional argument ${\mathbf{Feasibility\; Tolerance}}$ is $\delta $. Over a period of $K$ iterations (where $K$ is the value of the optional argument ${\mathbf{Expand\; Frequency}}$), the feasibility tolerance actually used by the function (i.e., the working feasibility tolerance) increases from $0.5\delta $ to $\delta $ (in steps of $0.5\delta /K$).
At certain stages the following ‘resetting procedure’ is used to remove small constraint infeasibilities. First, all nonbasic variables are moved exactly onto their bounds. A count is kept of the number of nontrivial adjustments made. If the count is nonzero, the basic variables are recomputed. Finally, the working feasibility tolerance is reinitialized to $0.5\delta $.
If a problem requires more than $K$ iterations, the resetting procedure is invoked and a new cycle of iterations is started. (The decision to resume the feasibility phase or optimality phase is based on comparing any constraint infeasibilities with $\delta $.)
The resetting procedure is also invoked when the function reaches an apparently optimal, infeasible or unbounded solution, unless this situation has already occurred twice. If any nontrivial adjustments are made, iterations are continued.
The EXPAND procedure not only allows a positive step to be taken at every iteration, but also provides a potential choice of constraints to be added to the working set. All constraints at a distance $\alpha $ (where $\alpha \le {\alpha}_{N}$) along $p$ from the current point are then viewed as acceptable candidates for inclusion in the working set. The constraint whose normal makes the largest angle with the search direction is added to the working set. This strategy helps keep the basis matrix $B$ wellconditioned.
12 Optional Arguments
Several optional arguments in nag_opt_sparse_convex_qp_solve (e04nqc) define choices in the problem specification or the algorithm logic. In order to reduce the number of formal arguments of nag_opt_sparse_convex_qp_solve (e04nqc) these optional arguments have associated default values that are appropriate for most problems. Therefore, you need only specify those optional arguments whose values are to be different from their default values.
The remainder of this section can be skipped if you wish to use the default values for all optional arguments.
The following is a list of the optional arguments available. A full description of each optional argument is provided in
Section 12.1.
nag_opt_sparse_convex_qp_option_set_file (e04nrc) reads options from an external options file, with
Begin and
End as the first and last lines respectively and each intermediate line defining a single optional argument. For example,
Begin
Print Level = 5
End
The call
e04nrc (ioptns, &state, &fail);
can then be used to read the file on
descriptor
ioptns.
${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR
on successful exit.
nag_opt_sparse_convex_qp_option_set_file (e04nrc) should be consulted for a full description of this method of supplying optional arguments.
All optional arguments not specified by you are set to their default values. Optional arguments specified by you are unaltered by nag_opt_sparse_convex_qp_solve (e04nqc) (unless they define invalid values) and so remain in effect for subsequent calls unless altered by you.
12.1 Description of the Optional Arguments
For each option, we give a summary line, a description of the optional argument and details of constraints.
The summary line contains:
 the keywords, where the minimum abbreviation of each keyword is underlined (if no characters of an optional qualifier are underlined, the qualifier may be omitted);
 a parameter value,
where the letters $a$, $i\text{ and}r$ denote options that take character, integer and real values respectively;
 the default value is used whenever the condition $\lefti\right\ge 100000000$ is satisfied and where the symbol $\epsilon $ is a generic notation for machine precision (see nag_machine_precision (X02AJC));
 The variable $\mathit{bigbnd}$ holds the value of ${\mathbf{Infinite\; Bound\; Size}}$.
Keywords and character values are case and white space insensitive.
Optional arguments used to specify files (e.g., optional arguments
${\mathbf{Dump\; File}}$ and
${\mathbf{Print\; File}}$) have type Nag_FileID (see
Section 3.2.1.1 in the Essential Introduction). This ID value must either be set to
$0$ (the default value) in which case there will be no output, or will be as returned by a call of
nag_open_file (x04acc).
Check Frequency  $i$  Default $\text{}=60$ 
Every $i$th iteration after the most recent basis factorization, a numerical test is made to see if the current solution $\left(x,s\right)$ satisfies the linear constraints $Axs=0$. If the largest element of the residual vector $r=Axs$ is judged to be too large, the current basis is refactorized and the basic variables recomputed to satisfy the constraints more accurately. If $i\le 0$, the value $i=99999999$ is used and effectively no checks are made.
${\mathbf{Check\; Frequency}}=1$ is useful for debugging purposes, but otherwise this option should not be needed.
Crash Option  $i$  Default $\text{}=3$ 
Crash Tolerance  $r$  Default $\text{}=0.1$ 
Note that these options do not apply when
${\mathbf{start}}=\mathrm{Nag\_Warm}$ (see
Section 5).
If
${\mathbf{start}}=\mathrm{Nag\_Cold}$, an internal Crash procedure is used to select an initial basis from various rows and columns of the constraint matrix
$\left(\begin{array}{cc}A& I\end{array}\right)$. The value of
$i$ determines which rows and columns of
$A$ are initially eligible for the basis, and how many times the Crash procedure is called. Columns of
$I$ are used to pad the basis where necessary.
$i$ 
Meaning 
$0$ 
The initial basis contains only slack variables: $B=I$. 
$1$ 
The Crash procedure is called once, looking for a triangular basis in all rows and columns of the matrix $A$. 
$2$ 
The Crash procedure is called once, looking for a triangular basis in rows. 
$3$ 
The Crash procedure is called twice, treating linear equalities and linear inequalities separately. 
If $i\ge 1$, certain slacks on inequality rows are selected for the basis first. (If $i\ge 2$, numerical values are used to exclude slacks that are close to a bound.) The Crash procedure then makes several passes through the columns of $A$, searching for a basis matrix that is essentially triangular. A column is assigned to ‘pivot’ on a particular row if the column contains a suitably large element in a row that has not yet been assigned. (The pivot elements ultimately form the diagonals of the triangular basis.) For remaining unassigned rows, slack variables are inserted to complete the basis.
The ${\mathbf{Crash\; Tolerance}}$ allows the Crash procedure to ignore certain ‘small’ nonzero elements in each column of $A$. If ${a}_{\mathrm{max}}$ is the largest element in column $j$, other nonzeros ${a}_{ij}$ in the column are ignored if $\left{a}_{ij}\right\le {a}_{\mathrm{max}}\times r$. (To be meaningful, $r$ should be in the range $0\le r<1$.)
When $r>0.0$, the basis obtained by the Crash procedure may not be strictly triangular, but it is likely to be nonsingular and almost triangular. The intention is to obtain a starting basis containing more columns of $A$ and fewer (arbitrary) slacks. A feasible solution may be reached sooner on some problems.
For example, suppose the first $m$ columns of $A$ form the matrix shown under ${\mathbf{LU\; Factor\; Tolerance}}$; i.e., a tridiagonal matrix with entries $1$, $4$, $1$. To help the Crash procedure choose all $m$ columns for the initial basis, we would specify a ${\mathbf{Crash\; Tolerance}}$ of $r$ for some value of $r>0.5$.
This special keyword may be used to reset all optional arguments to their default values.
Dump File  ${i}_{1}$  Default $\text{}=0$ 
Load File  ${i}_{2}$  Default $\text{}=0$ 
(See
Section 3.2.1.1 in the Essential Introduction for further information on NAG data types.)
Optional arguments
${\mathbf{Dump\; File}}$ and
${\mathbf{Load\; File}}$ are similar to optional arguments
${\mathbf{Punch\; File}}$ and
${\mathbf{Insert\; File}}$, but they record solution information in a manner that is more direct and more easily modified. A full description of information recorded in optional arguments
${\mathbf{Dump\; File}}$ and
${\mathbf{Load\; File}}$ is given in
Gill et al. (2005a).
If ${\mathbf{Dump\; File}}>0$, the last solution obtained will be output to the file ${\mathbf{Dump\; File}}$.
If ${\mathbf{Load\; File}}>0$, the ${\mathbf{Load\; File}}$ containing basis information will be read.
The file will usually have been output previously as a ${\mathbf{Dump\; File}}$. The file will not be accessed if optional arguments ${\mathbf{Old\; Basis\; File}}$ or ${\mathbf{Insert\; File}}$ are specified.
Elastic Mode  $i$  Default $\text{}=1$ 
This argument determines if (and when) elastic mode is to be started. Three elastic modes are available as follows:
$i$ 
Meaning 
$0$ 
Elastic mode is never invoked. nag_opt_sparse_convex_qp_solve (e04nqc) will terminate as soon as infeasibility is detected. There may be other points with significantly smaller sums of infeasibilities. 
$1$ 
Elastic mode is invoked only if the constraints are found to be infeasible (the default). If the constraints are infeasible, continue in elastic mode with the composite objective determined by the values of the optional arguments ${\mathbf{Elastic\; Objective}}$ and ${\mathbf{Elastic\; Weight}}$. 
$2$ 
The iterations start and remain in elastic mode. This option allows you to minimize the composite objective function directly without first performing Phase 1 iterations.
The success of this option will depend critically on your choice of ${\mathbf{Elastic\; Weight}}$. If ${\mathbf{Elastic\; Weight}}$ is sufficiently large and the constraints are feasible, the minimizer of the composite objective and the solution of the original problem are identical. However, if the ${\mathbf{Elastic\; Weight}}$ is not sufficiently large, the minimizer of the composite function may be infeasible, even if a feasible point exists.

Elastic Objective  $i$  Default $\text{}=1$ 
This determines the form of the composite objective
$f\left(x\right)+\gamma {\displaystyle \sum _{j}}\phantom{\rule{0.25em}{0ex}}\left({v}_{j}+{w}_{j}\right)$ in Phase 2 (
$\gamma $). Three types of composite objectives are available.
$i$ 
Meaning 
$0$ 
Include only the true objective $f\left(x\right)$ in the composite objective. This option sets $\gamma =0$ in the composite objective and allows nag_opt_sparse_convex_qp_solve (e04nqc) to ignore the elastic bounds and find a solution that minimizes $f\left(x\right)$ subject to the nonelastic constraints. This option is useful if there are some ‘soft’ constraints that you would like to ignore if the constraints are infeasible. 
$1$ 
Use a composite objective defined with $\gamma $ determined by the value of ${\mathbf{Elastic\; Weight}}$. This value is intended to be used in conjunction with ${\mathbf{Elastic\; Mode}}=2$. 
$2$ 
Include only the elastic variables in the composite objective. The elastics are weighted by $\gamma =1$. This choice minimizes the violations of the elastic variables at the expense of possibly increasing the true objective. This option can be used to find a point that minimizes the sum of the violations of a subset of constraints specified by the input array helast. 
Elastic Weight  $r$  Default $\text{}=1.0$ 
This defines the value of $\gamma $ in the composite objective in Phase 2 ($\gamma $).
At each iteration of elastic mode, the composite objective is defined to be
where
$\sigma =1$ for
${\mathbf{Minimize}}$,
$\sigma =1$ for
${\mathbf{Maximize}}$, and
$f\left(x\right)$ is the quadratic objective.
Note that the effect of $\gamma $ is not disabled once a feasible point is obtained.
Expand Frequency  $i$  Default $\text{}=10000$ 
This option is part of an anticycling procedure (see
Section 11.4) designed to allow progress even on highly degenerate problems.
The strategy is to force a positive step at every iteration, at the expense of violating the constraints by a small amount. Suppose that the value of the optional argument ${\mathbf{Feasibility\; Tolerance}}$ is $\delta $. Over a period of $i$ iterations, the feasibility tolerance actually used by nag_opt_sparse_convex_qp_solve (e04nqc) (i.e., the working feasibility tolerance) increases from $0.5\delta $ to $\delta $ (in steps of $0.5\delta /i$).
Increasing the value of $i$ helps reduce the number of slightly infeasible nonbasic variables (most of which are eliminated during the resetting procedure). However, it also diminishes the freedom to choose a large pivot element (see the description of the optional argument ${\mathbf{Pivot\; Tolerance}}$).
If $i\le 0$, the value $i=99999999$ is used and effectively no anticycling procedure is invoked.
Factorization Frequency  $i$  Default $\text{}=100\left(\mathrm{LP}\right)$ or $50\left(\mathrm{QP}\right)$ 
If $i>0$, at most $i$ basis changes will occur between factorizations of the basis matrix.
For LP problems, the basis factors are usually updated at every iteration. Higher values of $i$ may be more efficient on problems that are extremely sparse and well scaled.
For QP problems, fewer basis updates will occur as the solution is approached. The number of iterations between basis factorizations will therefore increase. During these iterations a test is made regularly according to the value of optional argument ${\mathbf{Check\; Frequency}}$ to ensure that the linear constraints $Axs=0$ are satisfied. Occasionally, the basis will be refactorized before the limit of $i$ updates is reached. If $i\le 0$, the default value is used.
Feasibility Tolerance  $r$  Default $\text{}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left\{{10}^{6},\sqrt{\epsilon}\right\}$ 
A feasible problem is one in which all variables satisfy their upper and lower bounds to within the absolute tolerance $r$. (This includes slack variables. Hence, the general constraints are also satisfied to within $r$.)
nag_opt_sparse_convex_qp_solve (e04nqc) attempts to find a feasible solution before optimizing the objective function. If the sum of infeasibilities cannot be reduced to zero, the problem is assumed to be infeasible. Let sInf be the corresponding sum of infeasibilities. If sInf is quite small, it may be appropriate to raise $r$ by a factor of $10$ or $100$. Otherwise, some error in the data should be suspected.
Note that if sInf is not small and you have not asked nag_opt_sparse_convex_qp_solve (e04nqc) to minimize the violations of the elastic variables (i.e., you have not specified ${\mathbf{Elastic\; Objective}}=2$), there may be other points that have a significantly smaller sum of infeasibilities. nag_opt_sparse_convex_qp_solve (e04nqc) will not attempt to find the solution that minimizes the sum unless ${\mathbf{Elastic\; Objective}}=2$.
If the constraints and variables have been scaled (see the description of the optional argument ${\mathbf{Scale\; Option}}$), then feasibility is defined in terms of the scaled problem (since it is more likely to be meaningful).
Infinite Bound Size  $r$  Default $\text{}={10}^{20}$ 
If $r\ge 0$, $r$ defines the ‘infinite’ bound $\mathit{infbnd}$ in the definition of the problem constraints. Any upper bound greater than or equal to $\mathit{infbnd}$ will be regarded as $+\infty $ (and similarly any lower bound less than or equal to $\mathit{infbnd}$ will be regarded as $\infty $). If $r<0$, the default value is used.
Iterations Limit  $i$  Default $\text{}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left\{10000,10\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left\{m,n\right\}\right\}$ 
The value of $i$ specifies the maximum number of iterations allowed before termination. Setting $i=0$ and ${\mathbf{Print\; Level}}>0$ means that: the workspace needed to start solving the problem will be computed and printed; and feasibility and optimality will be checked. No iterations will be performed. If $i<0$, the default value is used.
LU Density Tolerance  ${r}_{1}$  Default
$\text{}=0.6$ 
LU Singularity Tolerance  ${r}_{2}$  Default
$\text{}={\epsilon}^{\frac{2}{3}}$ 
The density tolerance ${r}_{1}$ is used during $LU$ factorization of the basis matrix. Columns of $L$ and rows of $U$ are formed one at a time, and the remaining rows and columns of the basis are altered appropriately. At any stage, if the density of the remaining matrix exceeds ${r}_{1}$, the Markowitz strategy for choosing pivots is terminated. The remaining matrix is factored by a dense $LU$ procedure. Raising the density tolerance towards $1.0$ may give slightly sparser $LU$ factors, with a slight increase in factorization time.
If ${r}_{2}>0$, ${r}_{2}$ defines the singularity tolerance used to guard against illconditioned basis matrices. After $B$ is refactorized, the diagonal elements of $U$ are tested as follows. If $\left{u}_{jj}\right\le {r}_{2}$ or $\left{u}_{jj}\right<{r}_{2}{\displaystyle \underset{i}{\mathrm{max}}}\phantom{\rule{0.25em}{0ex}}\left{u}_{ij}\right$, the $j$th column of the basis is replaced by the corresponding slack variable. If ${r}_{2}\le 0$, the default value is used.
LU Factor Tolerance  ${r}_{1}$  Default $\text{}=100.0$ 
LU Update Tolerance  ${r}_{2}$  Default $\text{}=10.0$ 
The values of
${r}_{1}$ and
${r}_{2}$ affect the stability and sparsity of the basis factorization
$B=LU$, during refactorization and updates respectively. The lower triangular matrix
$L$ is a product of matrices of the form
where the multipliers
$\mu $ will satisfy
$\left\mu \right\le {r}_{i}$. The default values of
${r}_{1}$ and
${r}_{2}$ usually strike a good compromise between stability and sparsity. They must satisfy
${r}_{1}$,
${r}_{2}\ge 1.0$.
For large and relatively dense problems, ${r}_{1}=10.0\text{ or}5.0$ (say) may give a useful improvement in stability without impairing sparsity to a serious degree.
For certain very regular structures (e.g., band matrices) it may be necessary to reduce
${r}_{1}\text{ and/or}{r}_{2}$ in order to achieve stability. For example, if the columns of
$A$ include a submatrix of the form
one should set both
${r}_{1}$ and
${r}_{2}$ to values in the range
$1.0\le {r}_{i}<4.0$.
LU Partial Pivoting   Default 
The $LU$ factorization implements a Markowitztype search for pivots that locally minimize the fillin subject to a threshold pivoting stability criterion. The default option is to use threshold partial pivoting. The options ${\mathbf{LU\; Complete\; Pivoting}}$ and ${\mathbf{LU\; Rook\; Pivoting}}$ are more expensive but more stable and better at revealing rank, as long as the ${\mathbf{LU\; Factor\; Tolerance}}$ is not too large (say $<2.0$).
This option specifies the required direction of the optimization. It applies to both linear and nonlinear terms (if any) in the objective function. Note that if two problems are the same except that one minimizes
$f\left(x\right)$ and the other maximizes
$f\left(x\right)$, their solutions will be the same but the signs of the dual variables
${\pi}_{i}$ and the reduced gradients
${d}_{j}$ (see
Section 11.3) will be reversed.
The option ${\mathbf{Feasible\; Point}}$ means ‘ignore the objective function, while finding a feasible point for the linear constraints’. It can be used to check that the constraints are feasible without altering the call to nag_opt_sparse_convex_qp_solve (e04nqc).
New Basis File  ${i}_{1}$  Default $\text{}=0$ 
Backup Basis File  ${i}_{2}$  Default $\text{}=0$ 
Save Frequency  ${i}_{3}$  Default $\text{}=100$ 
(See
Section 3.2.1.1 in the Essential Introduction for further information on NAG data types.)
Optional arguments ${\mathbf{New\; Basis\; File}}$ and ${\mathbf{Backup\; Basis\; File}}$ are sometimes referred to as basis maps. They contain the most compact representation of the state of each variable. They are intended for restarting the solution of a problem at a point that was reached by an earlier run. For nontrivial problems, it is advisable to save basis maps at the end of a run, in order to restart the run if necessary.
If ${\mathbf{New\; Basis\; File}}>0$, a basis map will be saved on file ${\mathbf{New\; Basis\; File}}$ every ${i}_{3}$th iteration, where ${i}_{3}$ is the ${\mathbf{Save\; Frequency}}$.
The first record of the file will contain the word PROCEEDING if the run is still in progress. A basis map will also be saved at the end of a run, with some other word indicating the final solution status.
If ${\mathbf{Backup\; Basis\; File}}>0$, ${\mathbf{Backup\; Basis\; File}}$ is intended as a safeguard against losing the results of a long run. Suppose that a ${\mathbf{New\; Basis\; File}}$ is being saved every $100$ (${\mathbf{Save\; Frequency}}$) iterations, and that nag_opt_sparse_convex_qp_solve (e04nqc) is about to save such a basis at iteration $2000$. It is conceivable that the run may be interrupted during the next few milliseconds (in the middle of the save). In this case the Basis file will be corrupted and the run will have been essentially wasted.
To eliminate this risk, both a
${\mathbf{New\; Basis\; File}}$ and a
${\mathbf{Backup\; Basis\; File}}$ may be specified. The following would be suitable for the above example:
Backup Basis FileID1
New Basis FileID2
where
FileID1 and
FileID2 are returned by
nag_open_file (x04acc).
The current basis will then be saved every $100$ iterations, first on FileID2 and then immediately on FileID1. If the run is interrupted at iteration $2000$ during the save on FileID2, there will still be a usable basis on FileID1 (corresponding to iteration $1900$).
Note that a new basis will be saved in ${\mathbf{New\; Basis\; File}}$ at the end of a run if it terminates normally, but it will not be saved in ${\mathbf{Backup\; Basis\; File}}$. In the above example, if an optimum solution is found at iteration $2050$ (or if the iteration limit is $2050$), the final basis on FileID2 will correspond to iteration $2050$, but the last basis saved on FileID1 will be the one for iteration $2000$.
A full description of information recorded in
${\mathbf{New\; Basis\; File}}$ and
${\mathbf{Backup\; Basis\; File}}$ is given in
Gill et al. (2005a).
Normally each optional argument specification is printed to unit ${\mathbf{Print\; File}}$ as it is supplied. Optional argument ${\mathbf{Nolist}}$ may be used to suppress the printing and optional argument ${\mathbf{List}}$ may be used to restore printing.
Old Basis File  $i$  Default $\text{}=0$ 
(See
Section 3.2.1.1 in the Essential Introduction for further information on NAG data types.)
If
${\mathbf{Old\; Basis\; File}}>0$, the basis maps information will be obtained from the file associated with ID
$i$.
The file will usually have been output previously as a
${\mathbf{New\; Basis\; File}}$ or
${\mathbf{Backup\; Basis\; File}}$.
A full description of information recorded in
${\mathbf{New\; Basis\; File}}$ and
${\mathbf{Backup\; Basis\; File}}$ is given in
Gill et al. (2005a).
The file will not be acceptable if the number of rows or columns in the problem has been altered.
Optimality Tolerance  $r$  Default $\text{}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left\{{10}^{6},\sqrt{\epsilon}\right\}$ 
This is used to judge the size of the reduced gradients ${d}_{j}={g}_{j}{a}_{j}^{\mathrm{T}}\pi $, where ${g}_{j}$ is the $j$th component of the gradient, ${a}_{j}$ is the associated column of the constraint matrix $\left(\begin{array}{cc}A& I\end{array}\right)$, and $\pi $ is the set of dual variables.
By construction, the reduced gradients for basic variables are always zero. The problem will be declared optimal if the reduced gradients for nonbasic variables at their lower or upper bounds satisfy
respectively, and if
$\left{d}_{j}\right/\Vert \pi \Vert \le r$ for superbasic variables.
In the above tests,
$\Vert \pi \Vert $ is a measure of the size of the dual variables. It is included to make the tests independent of a scale factor on the objective function. The quantity
$\Vert \pi \Vert $ actually used is defined by
so that only large scale factors are allowed for.
If the objective is scaled down to be very small, the optimality test reduces to comparing ${d}_{j}$ against $0.01r$.
Partial Price  $i$  Default $\text{}=10\left(\mathrm{LP}\right)$ or $1\left(\mathrm{QP}\right)$ 
This option is recommended for large FP or LP problems that have significantly more variables than constraints (i.e., $n\gg m$). It reduces the work required for each pricing operation (i.e., when a nonbasic variable is selected to enter the basis). If $i=1$, all columns of the constraint matrix $\left(\begin{array}{cc}A& I\end{array}\right)$ are searched. If $i>1$, $A$ and $I$ are partitioned to give $i$ roughly equal segments ${A}_{\mathit{j}},{I}_{\mathit{j}}$, for $\mathit{j}=1,2,\dots ,i$ (modulo $i$). If the previous pricing search was successful on ${A}_{j1},{I}_{j1}$, the next search begins on the segments ${A}_{j}$ and ${I}_{j}$. If a reduced gradient is found that is larger than some dynamic tolerance, the variable with the largest such reduced gradient (of appropriate sign) is selected to enter the basis. If nothing is found, the search continues on the next segments ${A}_{j+1},{I}_{j+1}$, and so on. If $i\le 0$, the default value is used.
Pivot Tolerance  $r$  Default $\text{}={\epsilon}^{\frac{2}{3}}$ 
Broadly speaking, the pivot tolerance is used to prevent columns entering the basis if they would cause the basis to become almost singular.
When $x$ changes to $x+\alpha p$ for some search direction $p$, a ‘ratio test’ determines which component of $x$ reaches an upper or lower bound first. The corresponding element of $p$ is called the pivot element. Elements of $p$ are ignored (and therefore cannot be pivot elements) if they are smaller than the pivot tolerance $r$.
It is common for two or more variables to reach a bound at essentially the same time. In such cases, the optional argument ${\mathbf{Feasibility\; Tolerance}}$ (say $t$) provides some freedom to maximize the pivot element and thereby improve numerical stability. Excessively small values of $t$ should therefore not be specified. To a lesser extent, the optional argument ${\mathbf{Expand\; Frequency}}$ (say $f$) also provides some freedom to maximize the pivot element. Excessively large values of $f$ should therefore not be specified.
Print File  $i$  Default $\text{}=0$ 
(See
Section 3.2.1.1 in the Essential Introduction for further information on NAG data types.)
If
${\mathbf{Print\; File}}>0$, the following information is output to
${\mathbf{Print\; File}}$ during the solution of each problem:
– 
a listing of the optional arguments; 
– 
some statistics about the problem; 
– 
the amount of storage available for the $LU$ factorization of the basis matrix; 
– 
notes about the initial basis resulting from a Crash procedure or a Basis file; 
– 
the iteration log; 
– 
basis factorization statistics; 
– 
the exit fail condition and some statistics about the solution obtained; 
– 
the printed solution, if requested. 
The last four items are described in
Sections 9 and
13. Further brief output may be directed to the
${\mathbf{Summary\; File}}$.
Print Frequency  $i$  Default $\text{}=100$ 
If $i>0$, one line of the iteration log will be printed every $i$th iteration. A value such as $i=10$ is suggested for those interested only in the final solution. If $i\le 0$, the value of $i=99999999$ is used and effectively no checks are made.
Print Level  $i$  Default $\text{}=1$ 
This controls the amount of printing produced by nag_opt_sparse_convex_qp_solve (e04nqc) as follows.
$i$ 
Meaning 
0 
No output except error messages. If you want to suppress all output, set ${\mathbf{Print\; File}}=0$. 
$=1$ 
The set of selected options, problem statistics, summary of the scaling procedure, information about the initial basis resulting from a Crash or a Basis file, a single line of output at each iteration (controlled by the optional argument ${\mathbf{Print\; Frequency}}$), and the exit condition with a summary of the final solution. 
$\ge 10$ 
Basis factorization statistics. 
Punch File  ${i}_{1}$  Default $=0$ 
Insert File  ${i}_{2}$  Default $=0$ 
(See
Section 3.2.1.1 in the Essential Introduction for further information on NAG data types.)
These files provide compatibility with commercial mathematical programming systems. The
${\mathbf{Punch\; File}}$ from a previous run may be used as an
${\mathbf{Insert\; File}}$ for a later run on the same problem. A full description of information recorded in
${\mathbf{Insert\; File}}$ and
${\mathbf{Punch\; File}}$ is given in
Gill et al. (2005a).
If ${\mathbf{Insert\; File}}>0$, the final solution obtained will be output to file ${\mathbf{Punch\; File}}$.
For linear programs, this format is compatible with various commercial systems.
If ${\mathbf{Punch\; File}}>0$,
the ${\mathbf{Insert\; File}}$ containing basis information will be read. The file will usually have been output previously as a ${\mathbf{Punch\; File}}$. The file will not be accessed if ${\mathbf{Old\; Basis\; File}}$ is specified.
Specifies the activeset algorithm used to solve the quadratic program in Phase 2 ($\gamma $). ${\mathbf{QPSolver\; Cholesky}}$ holds the full Cholesky factor $R$ of the reduced Hessian ${Z}^{\mathrm{T}}HZ$. As the QP iterations proceed, the dimension of $R$ changes with the number of superbasic variables. If the number of superbasic variables needs to increase beyond the value of ${\mathbf{Reduced\; Hessian\; Dimension}}$, the reduced Hessian cannot be stored and the solver switches to ${\mathbf{QPSolver\; CG}}$. The Cholesky solver is reactivated if the number of superbasics stabilizes at a value less than ${\mathbf{Reduced\; Hessian\; Dimension}}$.
${\mathbf{QPSolver\; QN}}$ solves the QP using a quasiNewton method. In this case, $R$ is the factor of a quasiNewton approximate Hessian.
${\mathbf{QPSolver\; CG}}$ uses an activeset method similar to ${\mathbf{QPSolver\; QN}}$, but uses the conjugategradient method to solve all systems involving the reduced Hessian.
The Cholesky QP solver is the most robust, but may require a significant amount of computation if there are many superbasics.
The quasiNewton QP solver does not require computation of the exact $R$ at the start of Phase 2 ($\gamma $). It may be appropriate when the number of superbasics is large but relatively few iterations are needed to reach a solution (e.g., if nag_opt_sparse_convex_qp_solve (e04nqc) is called with a Warm Start).
The conjugategradient QP solver is appropriate for problems with many degrees of freedom (say, more than $2000$ superbasics).
Reduced Hessian Dimension  $i$  Default $=1\left(\mathrm{LP}\right)\text{ or}\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(2000,{n}_{H}+1,n\right)\left(\mathrm{QP}\right)$ 
This specifies that an $i$ by $i$ triangular matrix $R$ (to define the reduced Hessian according to ${R}^{\mathrm{T}}R={Z}^{\mathrm{T}}HZ$). is to be available for use by the Cholesky QP solver.
Scale Option  $i$  Default $\text{}=2$ 
Scale Tolerance  $r$  Default $\text{}=0.9$ 
Three scale options are available as follows:
$i$ 
Meaning 
0 
No scaling. This is recommended if it is known that $x$ and the constraint matrix never have very large elements (say, larger than $100$). 
1 
The constraints and variables are scaled by an iterative procedure that attempts to make the matrix coefficients as close as possible to $1.0$ (see Fourer (1982)). This will sometimes improve the performance of the solution procedures. 
2 
The constraints and variables are scaled by the iterative procedure. Also, a certain additional scaling is performed that may be helpful if the righthand side $b$ or the solution $x$ is large. This takes into account columns of $\left(\begin{array}{cc}A& I\end{array}\right)$ that are fixed or have positive lower bounds or negative upper bounds. 
Optional argument
${\mathbf{Scale\; Tolerance}}$ affects how many passes might be needed through the constraint matrix. On each pass, the scaling procedure computes the ratio of the largest and smallest nonzero coefficients in each column:
If
$\underset{j}{\mathrm{max}}}\phantom{\rule{0.25em}{0ex}}{\rho}_{j$ is less than
$r$ times its previous value, another scaling pass is performed to adjust the row and column scales. Raising
$r$ from
$0.9$ to
$0.99$ (say) usually increases the number of scaling passes through
$A$. At most
$10$ passes are made. The value of
$r$ should lie in the range
$0<r<1$.
${\mathbf{Scale\; Print}}$ causes the row scales $r\left(i\right)$ and column scales $c\left(j\right)$ to be printed to ${\mathbf{Print\; File}}$, if ${\mathbf{System\; Information\; Yes}}$ has been specified. The scaled matrix coefficients are ${\stackrel{}{a}}_{ij}={a}_{ij}c\left(j\right)/r\left(i\right)$, and the scaled bounds on the variables and slacks are ${\stackrel{}{l}}_{j}={l}_{j}/c\left(j\right)$, ${\stackrel{}{u}}_{j}={u}_{j}/c\left(j\right)$, where $c\left(j\right)=r\left(jn\right)$ if $j>n$.
This option determines if the final obtained solution is to be output to the
${\mathbf{Print\; File}}$. Note that the ${\mathbf{Solution\; File}}$ option operates independently.
Solution File  $i$  Default $\text{}=0$ 
(See
Section 3.2.1.1 in the Essential Introduction for further information on NAG data types.)
If ${\mathbf{Solution\; File}}>0$, the final solution will be output to file ${\mathbf{Solution\; File}}$ (whether optimal or not).
To see more significant digits in the printed solution, it will sometimes be useful to make
${\mathbf{Solution\; File}}$.
Summary File  ${i}_{1}$  Default $\text{}=0$ 
Summary Frequency  ${i}_{2}$  Default $\text{}=100$ 
(See
Section 3.2.1.1 in the Essential Introduction for further information on NAG data types.)
If
${\mathbf{Summary\; File}}>0$, a brief log will be output to file
${\mathbf{Summary\; File}}$, including one line of information every
${i}_{2}$th iteration.
In an interactive environment, it is useful to direct this output to the terminal, to allow a run to be monitored online. (If something looks wrong, the run can be manually terminated.) Further details are given in
Section 13. If
${i}_{2}\le 0$, the value of
${i}_{2}=99999999$ is used and effectively no checks are made.
Superbasics Limit  $i$  Default $\text{}=1\left(\mathrm{LP}\right)\text{ or}\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left\{{n}_{H}+1,n\right\}\left(\mathrm{QP}\right)$ 
This places a limit on the storage allocated for superbasic variables. Ideally, $i$ should be set slightly larger than the ‘number of degrees of freedom’ expected at an optimal solution.
For linear programs, an optimum is normally a basic solution with no degrees of freedom. (The number of variables lying strictly between their bounds is no more than $m$, the number of general constraints.) The default value of $i$ is therefore $1$.
For quadratic problems, the number of degrees of freedom is often called the ‘number of independent variables’. Normally, $i$ need not be greater than ${n}_{H}+1$, where ${n}_{H}$ is the number of leading nonzero columns of $H$. For many problems, $i$ may be considerably smaller than ${n}_{H}$. This will save storage if ${n}_{H}$ is very large.
Normally nag_opt_sparse_convex_qp_solve (e04nqc) prints the options file as it is being read, and then prints a complete list of the available keywords and their final values. The optional argument ${\mathbf{Suppress\; Parameters}}$ tells nag_opt_sparse_convex_qp_solve (e04nqc) not to print the full list.
System Information No   Default 
This option prints additional information on the progress of major and minor iterations, and Crash statistics. See
Section 13.
Timing Level  $i$  Default $\text{}=0$ 
If $i>0$, some timing information will be output to the Print file, if ${\mathbf{Print\; File}}>0$.
Unbounded Step Size  $r$  Default $\text{}=\mathit{infbnd}$ 
If $r>0$, $r$ specifies the magnitude of the change in variables that will be considered a step to an unbounded solution. (Note that an unbounded solution can occur only when the Hessian is not positive definite.) If the change in $x$ during an iteration would exceed the value of $r$, the objective function is considered to be unbounded below in the feasible region. If $r\le 0$, the default value is used. See ${\mathbf{Infinite\; Bound\; Size}}$ for the definition of $\mathit{infbnd}$.
13 Description of Monitoring Information
This section describes the intermediate printout and final printout which constitutes the monitoring information produced by nag_opt_sparse_convex_qp_solve (e04nqc). (See also the description of the optional arguments ${\mathbf{Print\; File}}$ and ${\mathbf{Print\; Level}}$.) You can control the level of printed output.
13.1 Crash Statistics
When
${\mathbf{Print\; Level}}\ge 10$,
${\mathbf{Print\; File}}>0$ and
${\mathbf{System\; Information\; Yes}}$ has been specified, the following lines of intermediate printout (less than
$120$ characters) are produced on the unit number specified by optional argument
${\mathbf{Print\; File}}$ whenever
${\mathbf{start}}=\mathrm{Nag\_Cold}$ (see
Section 5). They refer to the number of columns selected by the Crash procedure during each of several passes through
$A$, whilst searching for a triangular basis matrix.
Label 
Description 
Slacks 
is the number of slacks selected initially.

Free cols 
is the number of free columns in the basis, including those whose bounds are rather far apart.

Preferred 
is the number of ‘preferred’ columns in the basis (i.e., ${\mathbf{hs}}\left[j1\right]=3$ for some $j\le n$). It will be a subset of the columns for which ${\mathbf{hs}}\left[j1\right]=3$ was specified.

Unit 
is the number of unit columns in the basis.

Double 
is the number of double columns in the basis.

Triangle 
is the number of triangular columns in the basis.

Pad 
is the number of slacks used to pad the basis (to make it a nonsingular triangle).

13.2 Basis Factorization Statistics
When
${\mathbf{Print\; Level}}\ge 10$ and
${\mathbf{Print\; File}}>0$, the first seven items of intermediate printout in the list below are produced on the unit number specified by optional argument
${\mathbf{Print\; File}}$ whenever the matrix
$B$ or
${B}_{S}={\left(\begin{array}{cc}B& S\end{array}\right)}^{\mathrm{T}}$ is factorized. Gaussian elimination is used to compute an
$LU$ factorization of
$B$ or
${B}_{S}$, where
$PL{P}^{\mathrm{T}}$ is a lower triangular matrix and
$PUQ$ is an upper triangular matrix for some permutation matrices
$P$ and
$Q$. The factorization is stabilized in the manner described under the optional argument
${\mathbf{LU\; Factor\; Tolerance}}$. In addition, if
${\mathbf{System\; Information\; Yes}}$ has been specified, the entries from
Elems onwards are also output.
Label 
Description 
Factor 
the number of factorizations since the start of the run. 
Demand 
a code giving the reason for the present factorization, as follows:
Code 
Meaning 
0 
First $LU$ factorization. 
1 
The number of updates reached the ${\mathbf{Factorization\; Frequency}}$. 
2 
The nonzeros in the updated factors have increased significantly. 
7 
Not enough storage to update factors. 
10 
Row residuals are too large (see the description of the optional argument ${\mathbf{Check\; Frequency}}$). 
11 
Illconditioning has caused inconsistent results. 

Itn 
is the current minor iteration number. 
Nonlin 
is the number of nonlinear variables in the current basis $B$. 
Linear 
is the number of linear variables in $B$. 
Slacks 
is the number of slack variables in $B$. 
B, BR, BS or BT factorize 
is the type of $LU$ factorization.
B 
periodic factorization of the basis $B$. 
BR 
more careful rankrevealing factorization of $B$ using threshold rook pivoting. This occurs mainly at the start, if the first basis factors seem singular or illconditioned. Followed by a normal B factorize. 
BS 
${B}_{S}$ is factorized to choose a wellconditioned $B$ from the current $\left(B\text{}S\right)$. Followed by a normal B factorize. 
BT 
same as BS except the current $B$ is tried first and accepted if it appears to be not much more illconditioned than after the previous BS factorize. 

m 
is the number of rows in $B$ or ${B}_{S}$. 
n 
is the number of columns in $B$ or ${B}_{S}$. Preceded by ‘=’ or ‘>’ respectively. 
Elems 
is the number of nonzero elements in $B$ or ${B}_{S}$. 
Amax 
is the largest nonzero in $B$ or ${B}_{S}$. 
Density 
is the percentage nonzero density of $B$ or ${B}_{S}$. 
Merit/MerRP/MerCP 
Merit is the average Markowitz merit count for the elements chosen to be the diagonals of $PUQ$. Each merit count is defined to be $\left(c1\right)\left(r1\right)$ where $c$ and $r$ are the number of nonzeros in the column and row containing the element at the time it is selected to be the next diagonal. Merit is the average of n such quantities. It gives an indication of how much work was required to preserve sparsity during the factorization. If ${\mathbf{LU\; Complete\; Pivoting}}$ or ${\mathbf{LU\; Rook\; Pivoting}}$ has been selected, this heading is changed to MerCP, respectively MerRP. 
lenL 
is the number of nonzeros in $L$. 
L+U 
is the number of nonzeros representing the basis factors $L$ and $U$. Immediately after a basis factorization $B=LU$, this is lenL+lenU, the number of subdiagonal elements in the columns of a lower triangular matrix and the number of diagonal and superdiagonal elements in the rows of an uppertriangular matrix. Further nonzeros are added to L when various columns of $B$ are later replaced. As columns of $B$ are replaced, the matrix $U$ is maintained explicitly (in sparse form). The value of L will steadily increase, whereas the value of U may fluctuate up or down. Thus the value of L+U may fluctuate up or down (in general, it will tend to increase). 
Cmpressns 
is the number of times the data structure holding the partially factored matrix needed to be compressed to recover unused storage. Ideally this number should be zero. If it is more than $3$ or $4$, the amount of workspace available to nag_opt_sparse_convex_qp_solve (e04nqc) should be increased for efficiency. 
Incres 
is the percentage increase in the number of nonzeros in $L$ and $U$ relative to the number of nonzeros in $B$ or ${B}_{S}$. 
Utri 
is the number of triangular rows of $B$ or ${B}_{S}$ at the top of $U$. 
lenU 
the number of nonzeros in $U$, including its diagonals. 
Ltol 
is the largest subdiagonal element allowed in $L$. This is the specified ${\mathbf{LU\; Factor\; Tolerance}}$ or a smaller value that is currently being used for greater stability. 
Umax 
the maximum nonzero element in $U$. 
Ugrwth 
is the ratio $\mathtt{Umax}/\mathtt{Amax}$, which ideally should not be substantially larger than $10.0$ or $100.0$. If it is orders of magnitude larger, it may be advisable to reduce the ${\mathbf{LU\; Factor\; Tolerance}}$ to $5.0$, $4.0$, $3.0$ or $2.0$, say (but bigger than $1.0$).
As long as Lmax is not large (say $5.0$ or less), $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(\mathtt{Amax},\mathtt{Umax}\right)/\mathtt{DUmin}$ gives an estimate of the condition number $B$. If this is extremely large, the basis is nearly singular. Slacks are used to replace suspect columns of $B$ and the modified basis is refactored. 
Ltri 
is the number of triangular columns of $B$ or ${B}_{S}$ at the left of $L$. 
dense1 
is the number of columns remaining when the density of the basis matrix being factorized reached $0.3$. 
Lmax 
is the actual maximum subdiagonal element in $L$ (bounded by Ltol). 
Akmax 
is the largest nonzero generated at any stage of the $LU$ factorization. (Values much larger than Amax indicate instability.) Akmax is not printed if ${\mathbf{LU\; Partial\; Pivoting}}$ is selected. 
Agrwth 
is the ratio $\mathtt{Akmax}/\mathtt{Amax}$. Values much larger than $100$ (say) indicate instability. Agrwth is not printed if ${\mathbf{LU\; Partial\; Pivoting}}$ is selected. 
bump 
is the size of the block to be factorized nontrivially after the triangular rows and columns of $B$ or ${B}_{S}$ have been removed. 
dense2 
is the number of columns remaining when the density of the basis matrix being factorized reached $0.6$. (The Markowitz pivot strategy searches fewer columns at that stage.) 
DUmax 
is the largest diagonal of $PUQ$. 
DUmin 
is the smallest diagonal of $PUQ$. 
condU 
the ratio $\mathtt{DUmax}/\mathtt{DUmin}$, which estimates the condition number of $U$ (and of $B$ if Ltol is less than $5.0$, say). 
13.3 Basis Map
When
${\mathbf{Print\; Level}}\ge 10$ and
${\mathbf{Print\; File}}>0$, the following lines of intermediate printout (less than
$80$ characters) are produced on the unit number specified by optional argument
${\mathbf{Print\; File}}$. They refer to the elements of the
names
array (see
Section 5).
Label 
Description 
Name 
gives the name for the problem (blank if problem unnamed).

Infeasibilities 
gives the number of infeasibilities. Printed only if the final point is infeasible. 
Objective Value 
gives the objective value at the final point (or the value of the sum of infeasibilities). Printed only if the final point is feasible. 
Status 
gives the exit status for the problem (i.e., Optimal soln, Weak soln, Unbounded, Infeasible, Excess itns, Error condn or Feasble soln) followed by details of the direction of the optimization (i.e., (Min) or (Max)).

Iteration 
gives the iteration number when the file was created. 
Superbasics 
gives the number of superbasic variables. 
Objective 
gives the name of the free row for the problem (blank if objective unnamed).

RHS 
gives the name of the constraint righthand side for the problem (blank if objective unnamed).

Ranges 
gives the name of the ranges for the problem (blank if objective unnamed).

Bounds 
gives the name of the bounds for the problem (blank if objective unnamed).

13.4 Solution Output
At the end of a run, the final solution will be output to the Print file. Some header information appears first to identify the problem and the final state of the optimization procedure. A ROWS section and a COLUMNS section then follow, giving one line of information for each row and column.
13.4.1 The ROWS section
General constraints take the form
$l\le Ax\le u$. The
$i$th constraint is therefore of the form
where
${\nu}_{i}$ is the
$i$th row of
$A$.
Internally, the constraints take the form
$Axs=0$, where
$s$ is the set of slack variables (which happen to satisfy the bounds
$l\le s\le u$). For the
$i$th constraint, the slack variable
${s}_{i}$ is directly available, and it is sometimes convenient to refer to its state. It should satisfy
$\alpha \le {s}_{i}\le \beta $. A fullstop (.) is printed for any numerical value that is exactly zero.
Label 
Description 
Number 
is the value of $n+i$. (This is used internally to refer to ${s}_{i}$ in the intermediate output.)

Row 
gives the name of ${\nu}_{i}$.

State 
the state of ${\nu}_{i}$ (the state of ${s}_{i}$ relative to the bounds $\alpha $ and $\beta $). The various states possible are as follows:
LL 
${s}_{i}$ is nonbasic at its lower limit, $\alpha $. 
UL 
${s}_{i}$ is nonbasic at its upper limit, $\beta $. 
EQ 
${s}_{i}$ is nonbasic and fixed at the value $\alpha =\beta $. 
FR 
${s}_{i}$ is nonbasic and currently zero, even though it is free to take any value between its bounds $\alpha $ and $\beta $. 
BS 
${s}_{i}$ is basic. 
SBS 
${s}_{i}$ is superbasic. 
A key is sometimes printed before State.
Note that unless the optional argument ${\mathbf{Scale\; Option}}=0$ is specified, the tests for assigning a key are applied to the variables of the scaled problem.
A 
Alternative optimum possible. The variable is nonbasic, but its reduced gradient is essentially zero. This means that if the variable were allowed to start moving away from its bound, there would be no change in the value of the objective function. The values of the other free variables might change, giving a genuine alternative solution. However, if there are any degenerate variables (labelled D), the actual change might prove to be zero, since one of them could encounter a bound immediately. In either case, the values of the Lagrange multipliers might also change.

D 
Degenerate. The variable is basic or superbasic, but it is equal (or very close) to one of its bounds.

I 
Infeasible. The variable is basic or superbasic and is currently violating one of its bounds by more than the value of the ${\mathbf{Feasibility\; Tolerance}}$.

N 
Not precisely optimal. If the slack is superbasic, the dual variable ${\pi}_{i}$ is not sufficiently small, as measured by the ${\mathbf{Optimality\; Tolerance}}$. If the slack is nonbasic, ${\pi}_{i}$ is not sufficiently positive or negative. If a loose ${\mathbf{Optimality\; Tolerance}}$ has been used, or if iterations were terminated before optimality, this key might be helpful in deciding whether or not to restart the run.


Activity 
is the value of ${\nu}_{i}x$ at the final iterate. 
Slack Activity 
is the value by which the row differs from its nearest bound. (For the free row (if any), it is set to Activity.)

Lower Limit 
is $\alpha $, the lower bound specified for the variable ${s}_{i}$. None indicates that ${\mathbf{bl}}\left[j1\right]\le \mathit{infbnd}$. 
Upper Limit 
is $\beta $, the upper bound specified for the variable ${s}_{i}$. None indicates that ${\mathbf{bu}}\left[j1\right]\ge \mathit{infbnd}$. 
Dual Activity 
is the value of the dual variable ${\pi}_{i}$ (the Lagrange multiplier for ${\nu}_{i}$; see Section 11.3). For FP problems, ${\pi}_{i}$ is set to zero.

i 
gives the index $i$ of the $i$th row.

13.4.2 The COLUMNS Section
Let the
$j$th component of
$x$ be the variable
${x}_{j}$ and assume that it satisfies the bounds
$\alpha \le {x}_{j}\le \beta $. A fullstop (.) is printed for any numerical value that is exactly zero.
Label 
Description 
Number 
is the column number $j$. (This is used internally to refer to ${x}_{j}$ in the intermediate output.)

Column 
gives the name of ${x}_{j}$.

State 
the state of ${x}_{j}$ relative to the bounds $\alpha $ and $\beta $. The various states possible are as follows:
LL 
${x}_{j}$ is nonbasic at its lower limit, $\alpha $. 
UL 
${x}_{j}$ is nonbasic at its upper limit, $\beta $. 
EQ 
${x}_{j}$ is nonbasic and fixed at the value $\alpha =\beta $. 
FR 
${x}_{j}$ is nonbasic and currently zero, even though it is free to take any value between its bounds $\alpha $ and $\beta $. 
BS 
${x}_{j}$ is basic. 
SBS 
${x}_{j}$ is superbasic. 
A key is sometimes printed before State.
Note that unless the optional argument ${\mathbf{Scale\; Option}}=0$ is specified, the tests for assigning a key are applied to the variables of the scaled problem.
A 
Alternative optimum possible. The variable is nonbasic, but its reduced gradient is essentially zero. This means that if the variable were allowed to start moving away from its bound, there would be no change in the value of the objective function. The values of the other free variables might change, giving a genuine alternative solution. However, if there are any degenerate variables (labelled D), the actual change might prove to be zero, since one of them could encounter a bound immediately. In either case, the values of the Lagrange multipliers might also change.

D 
Degenerate. The variable is basic or superbasic, but it is equal (or very close) to one of its bounds.

I 
Infeasible. The variable is basic or superbasic and is currently violating one of its bounds by more than the value of the ${\mathbf{Feasibility\; Tolerance}}$.

N 
Not precisely optimal. If the slack is superbasic, the dual variable ${\pi}_{i}$ is not sufficiently small, as measured by the ${\mathbf{Optimality\; Tolerance}}$. If the slack is nonbasic, ${\pi}_{i}$ is not sufficiently positive or negative. If a loose ${\mathbf{Optimality\; Tolerance}}$ has been used, or if iterations were terminated before optimality, this key might be helpful in deciding whether or not to restart the run.


Activity 
is the value of ${x}_{j}$ at the final iterate.

Obj Gradient 
is the value of ${g}_{j}$ at the final iterate. For FP problems, ${g}_{j}$ is set to zero.

Lower Limit 
is the lower bound specified for the variable. None indicates that ${\mathbf{bl}}\left[j1\right]\le \mathit{infbnd}$.

Upper Limit 
is the upper bound specified for the variable. None indicates that ${\mathbf{bu}}\left[j1\right]\ge \mathit{infbnd}$.

Reduced Gradnt 
is the value of ${d}_{j}$ at the final iterate (see Section 11.3). For FP problems, ${d}_{j}$ is set to zero.

m + j 
is the value of $m+j$.

Note: if two problems are the same except that one minimizes $f\left(x\right)$ and the other maximizes $f\left(x\right)$, their solutions will be the same but the signs of the dual variables ${\pi}_{i}$ and the reduced gradients ${d}_{j}$ will be reversed.
13.5 The Solution File
If ${\mathbf{Solution\; File}}>0$,
the information contained in a printed solution may also be output to the relevant file (which may be the Print file if so desired). Infinite Upper and Lower limits appear as $\pm {10}^{20}$ rather than None.
The maximum line length is $111$ characters.
A Solution file is intended to be read from disk by a selfcontained program that extracts and saves certain values as required for possible further computation. Typically the first
$14$ lines would be ignored.
The end of the ROWS section is marked by a line that starts with a
$1$ and is otherwise blank. If this and the next
$4$ lines are skipped, the COLUMNS section (see
Section 13.4.2) can then be read under the same format.
13.6 The Summary File
If ${\mathbf{Summary\; File}}>0$, certain brief information will be output to file. A disk file should be used to retain a concise log of each run if desired. (A ${\mathbf{Summary\; File}}$ is more easily perused than the associated ${\mathbf{Print\; File}}$).
The following information is included:
1. 
The optional arguments supplied via the option setting functions, if any; 
2. 
The Basis file loaded, if any; 
3. 
The status of the solution after each basis factorization (whether feasible; the objective value; the number of function calls so far); 
4. 
The same information every $k$th iteration, where $k$ is the specified ${\mathbf{Summary\; Frequency}}$; 
5. 
Warnings and error messages; 
6. 
The exit condition and a summary of the final solution. 
Item
4. is preceded by a blank line, but item
5. is not.
The meaning of the printout for linear constraints is the same as that given above for variables, with ‘variable’ replaced by ‘constraint’,
$n$ replaced by
$m$,
${\mathbf{names}}\left[j1\right]$ replaced by
${\mathbf{names}}\left[n+j1\right]$,
${\mathbf{bl}}\left[j1\right]$ and
${\mathbf{bu}}\left[j1\right]$ are replaced by
${\mathbf{bl}}\left[n+j1\right]$ and
${\mathbf{bu}}\left[n+j1\right]$ respectively, and with the following change in the heading:
Constrnt 
gives the name of the linear constraint.

Note that movement off a constraint (as opposed to a variable moving away from its bound) can be interpreted as allowing the entry in the Residual column to become positive.
Numerical values are output with a fixed number of digits; they are not guaranteed to be accurate to this precision.