NAG CL Interface
e04nkc (qpconvex1_sparse_solve)
1
Purpose
e04nkc solves sparse linear programming or convex quadratic programming problems.
2
Specification
void |
e04nkc (Integer n,
Integer m,
Integer nnz,
Integer iobj,
Integer ncolh,
const double a[],
const Integer ha[],
const Integer ka[],
const double bl[],
const double bu[],
double xs[],
Integer *ninf,
double *sinf,
double *obj,
Nag_E04_Opt *options,
Nag_Comm *comm,
NagError *fail) |
|
The function may be called by the names: e04nkc, nag_opt_qpconvex1_sparse_solve or nag_opt_sparse_convex_qp.
3
Description
e04nkc is designed to solve a class of quadratic programming problems that are assumed to be stated in the following general form:
where
is a set of variables,
is an
by
matrix and the objective function
may be specified in a variety of ways depending upon the particular problem to be solved. The optional parameter
(see
Section 12.2) may be used to specify an alternative problem in which
is maximized. The possible forms for
are listed in
Table 1 below, in which the prefixes FP, LP and QP stand for ‘feasible point’, ‘linear programming’ and ‘quadratic programming’ respectively,
is an
element vector and
is the
by
second-derivative matrix
(the
Hessian matrix).
Problem Type |
Objective Function |
Hessian Matrix |
FP |
Not applicable |
Not applicable |
LP |
|
Not applicable |
QP |
|
Symmetric positive semidefinite |
Table 1
For LP and QP problems, the unique global minimum value of is found. For FP problems, is omitted and the function attempts to find a feasible point for the set of constraints. For QP problems, a function must also be provided to compute for any given vector . ( need not be stored explicitly.)
e04nkc is intended to solve large-scale linear and quadratic programming problems in which the constraint matrix
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).
e04nkc also takes advantage of sparsity in
. (Sparsity in
can be exploited in the function that computes
.) For problems in which
can be treated as a
dense matrix, it is usually more efficient to use
e04mfc,
e04ncc or
e04nfc.
If
is positive definite, then the final
will be unique. If
e04nkc detects that
is indefinite, it terminates immediately with an error condition (see
Section 6). In that case, it may be more appropriate to call
e04ugc instead. If
is the zero matrix, the function will still solve the resulting LP problem; however, this can be accomplished more efficiently by setting the argument
(see
Section 5).
The upper and lower bounds on the
elements of
are said to define the
general constraints of the problem. Internally,
e04nkc converts the general constraints to equalities by introducing a set of
slack variables , where
. For example, the linear constraint
is replaced by
, together with the bounded slack
. The problem defined by
(1) can therefore be re-written in the following equivalent form:
Since the slack variables
are subject to the same upper and lower bounds as the elements of
, the bounds on
and
can simply be thought of as bounds on the combined vector
. (In order to indicate their special role in QP problems, the original variables
are sometimes known as ‘column variables’, and the slack variables
are known as ‘row variables’.)
Each LP or QP problem is solved using an active-set method. This is an iterative procedure with two phases: a feasibility phase, in which the sum of infeasibilities is minimized to find a feasible point; and an optimality phase, in which is minimized by constructing a sequence of iterations that lies within the feasible region.
A constraint is said to be active or binding at if the associated element of either or is equal to one of its upper or lower bounds. Since an active constraint in 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 . 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 active-set method, the constraints
are (conceptually) partitioned into the form
where
consists of the nonbasic elements of
and the
basis matrix is square and nonsingular. The elements of
and
are called the
basic and
superbasic variables respectively; with
they are a permutation of the elements of
and
. 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,
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
continues to satisfy
. The number of superbasic variables (
say) therefore indicates the number of degrees of freedom remaining after the constraints have been satisfied. In broad terms,
is a measure of
how nonlinear the problem is. In particular,
will always be zero for FP and LP problems.
If it appears that no improvement can be made with the current definition of , and , a nonbasic variable is selected to be added to , and the process is repeated with the value of 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 is decreased by one.
Associated with each of the
equality constraints
is a
dual variable . Similarly, each variable in
has an associated
reduced gradient (also known as a
reduced cost). The reduced gradients for the variables
are the quantities
, where
is the gradient of the QP objective function; and the reduced gradients for the slack variables
are the dual variables
. The QP subproblem is optimal if
for all nonbasic variables at their lower bounds,
for all nonbasic variables at their upper bounds and
for all superbasic variables. In practice, an
approximate QP solution is found by slightly relaxing these conditions on
(see the description of the optional parameter
in
Section 12.2).
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
means that the reduced gradient
associated with the relevant active upper or lower bound on
is computed via the formula
, where
is the
th column of
. (The variable selected by such a process and the corresponding value of
(i.e., its reduced gradient) are the quantities
+S and
dj in the detailed printed output from
e04nkc; see
Section 12.3.) If
has significantly more columns than rows (i.e.,
), 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
's.
e04nkc is based on SQOPT, which is part of the SNOPT package described in
Gill et al. (2002), which in turn utilizes routines from the MINOS package (see
Murtagh and Saunders (1995)). It uses stable numerical methods throughout and includes a reliable basis package (for maintaining sparse
factors of the basis matrix
), a practical anti-degeneracy procedure, efficient handling of linear constraints and bounds on the variables (by an active-set strategy), as well as automatic scaling of the constraints. Further details can be found in
Section 9.
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 (2002) SNOPT: An SQP Algorithm for Large-scale Constrained Optimization 12 979–1006 SIAM J. Optim.
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 anti-cycling procedure for linearly constrained optimization Math. Programming 45 437–474
Gill P E, Murray W, Saunders M A and Wright M H (1991) Inertia-controlling 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
Murtagh B A and Saunders M A (1995) MINOS 5.4 users' guide Report SOL 83-20R Department of Operations Research, Stanford University
5
Arguments
-
1:
– Integer
Input
-
On entry: , the number of variables (excluding slacks). This is the number of columns in the linear constraint matrix .
Constraint:
.
-
2:
– Integer
Input
-
On entry:
, the number of general linear constraints (or slacks). This is the number of rows in
, including the free row (if any; see argument
iobj).
Constraint:
.
-
3:
– Integer
Input
-
On entry: the number of nonzero elements in .
Constraint:
.
-
4:
– Integer
Input
-
On entry: if
, row
iobj of
is a free row containing the nonzero elements of the vector
appearing in the linear objective term
.
If
, there is no free row – i.e., the problem is either an FP problem (in which case
iobj must be set to zero), or a QP problem with
.
Constraint:
.
-
5:
– Integer
Input
-
On entry:
, the number of leading nonzero columns of the Hessian matrix
. For FP and LP problems,
ncolh must be set to zero.
Constraint:
.
-
6:
– function, supplied by the user
External Function
-
qphx must be supplied for QP problems to compute the matrix product
. If
has zero rows and columns, it is most efficient to order the variables
so that
where the nonlinear variables
appear first as shown. For FP and LP problems,
qphx will never be called and the NAG defined null function pointer,
NULLFN, can be supplied in the call to
e04nkc.
The specification of
qphx is:
void |
qphx (Integer ncolh,
const double x[],
double hx[],
Nag_Comm *comm)
|
|
-
1:
– Integer
Input
-
On entry: the number of leading nonzero columns of the Hessian matrix , as supplied to e04nkc.
-
2:
– const double
Input
-
On entry: the first
ncolh elements of
x.
-
3:
– double
Output
-
On exit: the product .
-
4:
– Nag_Comm *
-
Pointer to structure of type Nag_Comm; the following members are relevant to
qphx.
- first – Nag_BooleanInput
-
On entry: will be set to Nag_TRUE on the first call to
qphx and Nag_FALSE for all subsequent calls.
- nf – IntegerInput
-
On entry: the number of evaluations of the objective function; this value will be equal to the number of calls made to
qphx including the current one.
- user – double *
- iuser – Integer *
- p – Pointer
-
The type Pointer will be void * with a C compiler that defines void * or char *.
Before calling
e04nkc these pointers may be allocated memory and initialized with various quantities for use by
qphx when called from
e04nkc.
Note: qphx should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by
e04nkc. If your code inadvertently
does return any NaNs or infinities,
e04nkc is likely to produce unexpected results.
Note: qphx should be tested separately before being used in conjunction with
e04nkc. The array
x must
not be changed by
qphx.
-
7:
– const double
Input
-
On entry: the nonzero elements of
, ordered by increasing column index. Note that elements with the same row and column indices are not allowed. The row and column indices are specified by arguments
ha and
ka (see below).
-
8:
– const Integer
Input
-
On entry: must contain the row index of the nonzero element stored in , for . Note that the row indices for a column may be supplied in any order.
Constraint:
, for .
-
9:
– const Integer
Input
-
On entry:
must contain the index in
a of the start of the
th column, for
. To specify the
th column as empty, set
. Note that the first and last elements of
ka must be such that
and
.
Constraints:
- ;
- , for ;
- ;
- , for .
-
10:
– const double
Input
-
11:
– const double
Input
-
On entry:
bl must contain the lower bounds and
bu the upper bounds, for all the constraints in the following order. The first
elements of each array must contain the bounds on the variables, and the next
elements the bounds for the general linear constraints
and the free row (if any). To specify a nonexistent lower bound (i.e.,
), set
, and to specify a nonexistent upper bound (i.e.,
), set
, where
is one of the optional parameters (default value
, see
Section 12.2). To specify the
th constraint as an equality, set
, say, where
. Note that, for LP and QP problems, the lower bound corresponding to the free row must be set to
and stored in
; similarly, the upper bound must be set to
and stored in
.
Constraints:
- , for ;
- if , ;
- if , and
.
-
12:
– double
Input/Output
-
On entry:
, for
, must contain the initial values of the variables,
. In addition, if a ‘warm start’ is specified by means of the optional parameter
(see
Section 12.2) the elements
, for
, must contain the initial values of the slack variables,
.
On exit: the final values of the variables and slacks .
-
13:
– Integer *
Output
-
On exit: the number of infeasibilities. This will be zero if an optimal solution is found, i.e., if
e04nkc exits with
or
NW_SOLN_NOT_UNIQUE.
-
14:
– double *
Output
-
On exit: the sum of infeasibilities. This will be zero if
. (Note that
e04nkc does attempt to compute the minimum value of
sinf in the event that the problem is determined to be infeasible, i.e., when
e04nkc exits with
.)
-
15:
– double *
Output
-
On exit: the value of the objective function.
If
,
obj includes the quadratic objective term
(if any).
If
,
obj is just the linear objective term
(if any).
For FP problems,
obj is set to zero.
-
16:
– Nag_E04_Opt *
Input/Output
-
On entry/exit: a pointer to a structure of type Nag_E04_Opt whose members are optional parameters for
e04nkc. These structure members offer the means of adjusting some of the argument values of the algorithm and on output will supply further details of the results. A description of the members of
options is given below in
Section 12. Some of the results returned in
options can be used by
e04nkc to perform a ‘warm start’ (see the member
in
Section 12.2).
The
options structure also allows names to be assigned to the columns and rows (i.e., the variables and constraints) of the problem, which are then used in solution output.
If any of these optional parameters are required then the structure
options should be declared and initialized by a call to
e04xxc and supplied as an argument to
e04nkc. However, if the optional parameters are not required the NAG defined null pointer,
E04_DEFAULT, can be used in the function call.
-
17:
– Nag_Comm *
Input/Output
-
Note: comm is a NAG defined type (see
Section 3.1.1 in the Introduction to the NAG Library CL Interface).
On entry/exit: structure containing pointers for communication to the user-supplied function,
qphx, and the optional user-defined printing function; see the description of
qphx and
Section 12.3.1 for details. If you do not need to make use of this communication feature the null pointer
NAGCOMM_NULL may be used in the call to
e04nkc;
comm will then be declared internally for use in calls to user-supplied functions.
-
18:
– NagError *
Input/Output
-
The NAG error argument (see
Section 7 in the Introduction to the NAG Library CL Interface).
6
Error Indicators and Warnings
- NE_ALLOC_FAIL
-
Dynamic memory allocation failed.
- NE_ARRAY_CONS
-
The contents of array
ka are not valid.
Constraint:
, for
.
The contents of array
ka are not valid.
Constraint:
.
The contents of array
ka are not valid.
Constraint:
.
- NE_BAD_PARAM
-
On entry, argument had an illegal value.
On entry, argument had an illegal value.
On entry, argument had an illegal value.
On entry, argument had an illegal value.
- NE_BASIS_ILL_COND
-
Numerical error in trying to satisfy the general constraints. The basis is very ill conditioned.
- NE_BASIS_SINGULAR
-
The basis is singular after 15 attempts to factorize it.
The basis is singular after 15 attempts to factorize it (adding slacks where necessary). Either the problem is badly scaled or the value of the optional parameter
is too large; see
Section 12.2.
- NE_BOUND
-
The lower bound for variable (array element ) is greater than the upper bound.
- NE_BOUND_EQ
-
The lower bound and upper bound for variable (array elements and ) are equal but they are greater than or equal to .
- NE_BOUND_EQ_LCON
-
The lower bound and upper bound for linear constraint (array elements and ) are equal but they are greater than or equal to .
- NE_BOUND_LCON
-
The lower bound for linear constraint (array element ) is greater than the upper bound.
- NE_DUPLICATE_ELEMENT
-
Duplicate sparse matrix element found in row , column .
- NE_HESS_INDEF
-
The Hessian matrix appears to be indefinite.
The Hessian matrix
(see
Section 11.2) appears to be indefinite – normally because
is indefinite. Check that function
qphx has been coded correctly. If
qphx is coded correctly with
symmetric positive (semi-)definite, then the problem may be due to a loss of accuracy in the internal computation of the reduced Hessian. Try to reduce the values of the optional parameters
and
(see
Section 12.2).
- NE_HESS_TOO_BIG
-
Reduced Hessian exceeds assigned dimension. .
The reduced Hessian matrix
(see
Section 11.2) exceeds its assigned dimension. The value of the optional parameter
is too small; see
Section 12.2.
- NE_INT_ARG_LT
-
On entry, .
Constraint: .
On entry, .
Constraint: .
- NE_INT_ARRAY_1
-
Value
given to
not valid. Correct range for elements of
ka is
.
- NE_INT_ARRAY_2
-
Value
given to
not valid. Correct range for elements of
ha is 1 to
m.
- NE_INT_OPT_ARG_LT
-
On entry, .
Constraint: .
On entry, .
Constraint: .
On entry, .
Constraint: .
On entry, .
Constraint: .
On entry, .
Constraint: .
On entry, .
Constraint: .
- 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.
- NE_INVALID_INT_RANGE_1
-
Value
given to
iobj is not valid. Correct range is
.
Value
given to
ncolh is not valid. Correct range is
.
Value
given to
nnz is not valid. Correct range is
.
- NE_INVALID_INT_RANGE_2
-
Value given to is not valid. Correct range is .
- NE_INVALID_REAL_RANGE_F
-
Value given to is not valid. Correct range is .
Value given to is not valid. Correct range is .
Value given to is not valid. Correct range is .
Value given to is not valid. Correct range is .
Value given to is not valid. Correct range is .
Value given to is not valid. Correct range is .
Value given to is not valid. Correct range is .
Value given to is not valid. Correct range is .
- NE_INVALID_REAL_RANGE_FF
-
Value given to is not valid. Correct range is .
Value given to is not valid. Correct range is .
- NE_NAME_TOO_LONG
-
The string pointed to by is too long. It should be no longer than 8 characters.
- NE_NOT_APPEND_FILE
-
Cannot open file for appending.
- NE_NOT_CLOSE_FILE
-
Cannot close file .
- NE_NULL_QPHX
-
Since argument
ncolh is nonzero, the problem is assumed to be of type QP. However, the argument
qphx is a null function.
qphx must be non-null for QP problems.
- NE_OBJ_BOUND
-
Invalid lower bound for objective row. Bound should be .
Invalid upper bound for objective row. Bound should be .
- NE_OPT_NOT_INIT
-
Options structure not initialized.
- NE_OUT_OF_WORKSPACE
-
There is insufficient workspace for the basis factors, and the maximum allowed number of reallocation attempts, as specified by options.max_restart, has been reached.
- NE_STATE_VAL
-
is out of range. .
- NE_UNBOUNDED
-
Solution appears to be unbounded.
The problem is unbounded (or badly scaled). The objective function is not bounded below in the feasible region.
- NE_WRITE_ERROR
-
Error occurred when writing to file .
- NW_NOT_FEASIBLE
-
No feasible point was found for the linear constraints.
The problem is infeasible. The general constraints cannot all be satisfied simultaneously to within the value of the optional parameter
; see
Section 12.2.
- NW_SOLN_NOT_UNIQUE
-
Optimal solution is not unique.
Weak solution found. The final is not unique, although gives the global minimum value of the objective function.
- NW_TOO_MANY_ITER
-
The maximum number of iterations, , have been performed.
Too many iterations. The value of the optional parameter
is too small; see
Section 12.2.
7
Accuracy
e04nkc implements a numerically stable active set strategy and returns solutions that are as accurate as the condition of the problem warrants on the machine.
8
Parallelism and Performance
e04nkc is not threaded in any implementation.
None.
10
Example
To minimize the quadratic function
, where
subject to the bounds
and the general 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
is positive semidefinite.
The function to calculate
(
qphx in the argument list; see
Section 5) is
qphess.
The example program shows the use of the
options and
comm structures. The data for the example include a set of user-defined column and row names, and data for the Hessian in a sparse storage format (see
Section 10.2 for further details).
The
options structure is initialized by
e04xxc and the
member is assigned to the array of character strings into which the column and row names were read. The
member of
comm is used to pass the Hessian into
e04nkc for use by the function
qphess.
On return from
e04nkc, the Hessian data is perturbed slightly and two further options set, selecting a warm start and a reduced level of printout.
e04nkc is then called for a second time. Finally, the memory freeing function
e04xzc is used to free the memory assigned by
e04nkc to the pointers in the options structure. You must
not use the standard C function
free() for this purpose.
The sparse storage scheme used for the Hessian in this example is similar to that which
e04nkc uses for the constraint matrix
a, but since the Hessian is symmetric we need only store the lower triangle (including the diagonal) of the matrix. Thus, an array
hess contains the nonzero elements of the lower triangle arranged in order of increasing column index. The array
khess contains the indices in
hess of the first element in each column, and the array
hhess contains the row index associated with each element in
hess. To allow the data to be passed via the
member of
comm, a struct
HessianData is declared, containing pointer members which are assigned to the three arrays defining the Hessian. Alternative approaches would have been to use the
and
members of
comm to pass suitably partitioned arrays to
qphess, or to avoid the use of
comm altogether and declare the Hessian data as global. The storage scheme suggested here is for illustrative purposes only.
10.1
Program Text
10.2
Program Data
10.3
Program Results
11
Further Description
This section gives a detailed description of the algorithm used in
e04nkc. This, and possibly the next section,
Section 12, may be omitted if the more sophisticated features of the algorithm and software are not currently of interest.
11.1
Overview
e04nkc is based on an inertia-controlling 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 code. The two-phase nature of the algorithm is reflected by changing the function being minimized from the sum of infeasibilities (the quantity
Sinf described in
Section 12.3) to the quadratic objective function (the quantity
Objective, see
Section 12.3).
In general, an iterative process is required to solve a quadratic program. Given an iterate
in both the original variables
and the slack variables
, a new iterate
is defined by
where the
step length is a non-negative scalar (the printed quantity
Step, see
Section 12.3), and
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
, 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 parameter
; see
Section 12.2). The working set is the current prediction of the constraints that hold with equality at a solution of the LP or QP problem. Let
denote the number of constraints in the working set (including bounds), and let
denote the associated
by
working set matrix consisting of the
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
must satisfy the identity
This characterisation allows
to be computed using any
by
full-rank matrix
that spans the null space of
. (Thus,
and
.) The null space matrix
is defined from a sparse
factorization of part of
(see
(6) and
(7) below). The direction
will satisfy
(3) if
where
is any
-vector.
The working set contains the constraints and a subset of the upper and lower bounds on the variables . Since the gradient of a bound constraint or is a vector of all zeros except for in position , it follows that the working set matrix contains the rows of and the unit rows associated with the upper and lower bounds in the working set.
The working set matrix
can be represented in terms of a certain column partition of the matrix
. As in
Section 3 we partition the constraints
so that
where
is a square nonsingular basis and
,
and
are the basic, superbasic and nonbasic variables respectively. The nonbasic variables are equal to their upper or lower bounds at
, 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
(the quantity
Ns in the detailed printed output; see
Section 12.3). Given values of
and
, the basic variables
are adjusted so that
satisfies
(5).
If
is a permutation matrix such that
, then the working set matrix
satisfies
where
is the identity matrix with the same number of columns as
.
The null space matrix
is defined from a sparse
factorization of part of
. In particular,
is maintained in ‘reduced gradient’ form, using the LUSOL package (see
Gill et al. (1987)) to maintain sparse
factors of the basis matrix
that alters as the working set
changes. Given the permutation
, 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
and
are obtained by solving with
or
. This choice of
implies that
, the number of ‘degrees of freedom’ at
, is the same as
, the number of superbasic variables.
Let
and
denote the
reduced gradient and
reduced Hessian of the objective function:
where
is the objective gradient at
. Roughly speaking,
and
describe the first and second derivatives of an
-dimensional
unconstrained problem for the calculation of
. (The condition estimator of
is the quantity
Cond Hz in the detailed printed output; see
Section 12.3.)
At each iteration, an upper triangular factor is available such that . Normally, is computed from at the start of the optimality phase and then updated as the QP working set changes. For efficiency, the dimension of should not be excessive (say, ). This is guaranteed if the number of nonlinear variables is ‘moderate’.
If the QP problem contains linear variables,
is positive semidefinite and
may be singular with at least one zero diagonal element. However, an inertia-controlling strategy is used to ensure that only the last diagonal element of
can be zero. (See
Gill et al. (1991) for a discussion of a similar strategy for indefinite quadratic programming.)
If the initial is singular, enough variables are fixed at their current value to give a nonsingular . This is equivalent to including temporary bound constraints in the working set. Thereafter, can become singular only when a constraint is deleted from the working set (in which case no further constraints are deleted until becomes nonsingular).
11.3
The Main Iteration
If the reduced gradient is zero,
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
minimizes the quadratic objective function when the constraints in the working set are treated as equalities. At a constrained stationary point, Lagrange multipliers
are defined from the equations
A Lagrange multiplier
corresponding to an inequality constraint in the working set is said to be
optimal if
when the associated constraint is at its
upper bound, or if
when the associated constraint is at its
lower bound, where
depends on the value of the optional parameter
(see
Section 12.2). If a multiplier is non-optimal, 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
(see
Section 6).
The special form
(6) of the working set allows the multiplier vector
, the solution of
(9), to be written in terms of the vector
where
satisfies the equations
, and
denotes the basic elements of
. The elements of
are the Lagrange multipliers
associated with the equality constraints
. The vector
of nonbasic elements of
consists of the Lagrange multipliers
associated with the upper and lower bound constraints in the working set. The vector
of superbasic elements of
is the reduced gradient
in
(8). The vector
of basic elements of
is zero, by construction. (The Euclidean norm of
and the final values of
,
and
are the quantities
Norm rg,
Reduced Gradnt,
Obj Gradient and
Dual Activity in the detailed printed output; see
Section 12.3.)
If the reduced gradient is not zero, Lagrange multipliers need not be computed and the search direction is given by
(see
(7) and
(11)). The step length is chosen to maintain feasibility with respect to the satisfied constraints.
There are two possible choices for
, depending on whether or not
is singular. If
is nonsingular,
is nonsingular and
in
(4) is computed from the equations
where
is the reduced gradient at
. In this case,
is the minimizer of the objective function subject to the working set constraints being treated as equalities. If
is feasible,
is defined to be unity. In this case, the reduced gradient at
will be zero, and Lagrange multipliers are computed at the next iteration. Otherwise,
is set to
, the step to the ‘nearest’ constraint along
. This constraint is added to the working set at the next iteration.
If
is singular, then
must also be singular, and an inertia-controlling strategy is used to ensure that only the last diagonal element of
is zero. (See
Gill et al. (1991) for a discussion of a similar strategy for indefinite quadratic programming.) In this case,
satisfies
which allows the objective function to be reduced by any step of the form
, where
. The vector
is a direction of unbounded descent for the QP problem in the sense that the QP objective is linear and decreases without bound along
. If no finite step of the form
(where
) reaches a constraint not in the working set, the QP problem is unbounded and the function terminates immediately with
(see
Section 6). Otherwise,
is defined as the maximum feasible step along
and a constraint active at
is added to the working set for the next iteration.
11.4
Miscellaneous
If the basis matrix is not chosen carefully, the condition of the null space matrix
in
(7) could be arbitrarily high. To guard against this, the function implements a ‘basis repair’ feature in which the LUSOL package (see
Gill et al. (1987)) is used to compute the rectangular factorization
returning just the permutation
that makes
unit lower triangular. The pivot tolerance is set to require
, and the permutation is used to define
in
(6). It can be shown that
is likely to be little more than unity. Hence,
should be well conditioned
regardless of the condition of . This feature is applied at the beginning of the optimality phase if a potential
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
by a small amount.
Suppose that the value of the optional parameter
(see
Section 12.2) is
. Over a period of
iterations (where
is the value of the optional parameter
; see
Section 12.2), the feasibility tolerance actually used by
e04nkc (i.e., the
working feasibility tolerance) increases from
to
(in steps of
).
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 non-trivial adjustments made. If the count is nonzero, the basic variables are recomputed. Finally, the working feasibility tolerance is reinitialized to .
If a problem requires more than 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 .)
The resetting procedure is also invoked when e04nkc reaches an apparently optimal, infeasible or unbounded solution, unless this situation has already occurred twice. If any non-trivial 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 (where ) along 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 well conditioned.
12
Optional Parameters
A number of optional input and output arguments to
e04nkc are available through the structure argument
options, type Nag_E04_Opt. An argument may be selected by assigning an appropriate value to the relevant structure member; those arguments not selected will be assigned default values. If no use is to be made of any of the optional parameters you should use the NAG defined null pointer,
E04_DEFAULT, in place of
options when calling
e04nkc; the default settings will then be used for all arguments.
Before assigning values to
options directly the structure
must be initialized by a call to the function
e04xxc. Values may then be assigned to the structure members in the normal C manner.
After return from
e04nkc, the
options structure may only be re-used for future calls of
e04nkc if the dimensions of the new problem are the same. Otherwise, the structure must be cleared by a call of
e04xzc) and re-initialized by a call of
e04xxc before future calls. Failure to do this will result in unpredictable behaviour.
Option settings may also be read from a text file using the function
e04xyc in which case initialization of the
options structure will be performed automatically if not already done. Any subsequent direct assignment to the
options structure must
not be preceded by initialization.
If assignment of functions and memory to pointers in the
options structure is required, then this must be done directly in the calling program; they cannot be assigned using
e04xyc.
12.1
Optional Parameter Checklist and Default Values
For easy reference, the following list shows the members of
options which are valid for
e04nkc together with their default values where relevant. The number
is a generic notation for
machine precision (see
X02AJC).
Nag_Start start |
|
Boolean list |
Nag_TRUE |
Nag_PrintType print_level |
Nag_Soln_Iter |
char outfile[512] |
stdout |
void (*print_fun)() |
NULL |
char prob_name[9] |
'' |
char obj_name[9] |
'' |
char rhs_name[9] |
'' |
char range_name[9] |
'' |
char bnd_name[9] |
'' |
char **crnames |
NULL |
Boolean minimize |
Nag_TRUE |
Integer max_iter |
|
Nag_CrashType crash |
|
double crash_tol |
0.1 |
Nag_ScaleType scale |
|
double scale_tol |
0.9 |
double optim_tol |
|
double ftol |
|
Integer reset_ftol |
10000 |
Integer fcheck |
60 |
Integer factor_freq |
100 |
Integer partial_price |
10 |
double pivot_tol |
|
double lu_factor_tol |
100.0 |
double lu_update_tol |
10.0 |
double lu_sing_tol |
|
Integer max_sb |
|
double inf_bound |
|
double inf_step |
|
Integer *state |
size |
double *lambda |
size |
Integer nsb |
|
Integer iter |
|
Integer nf |
|
12.2
Description of the Optional Parameters
start – Nag_Start | | Default |
On entry: specifies how the initial working set is to be chosen.
- An internal Crash procedure will be used to choose an initial basis matrix, .
- You must provide a valid definition of every array element of the optional parameter (see below), probably obtained from a previous call of e04nkc, while, for QP problems, the optional parameter (see below) must retain its value from a previous call.
Constraint:
or .
list – Nag_Boolean | | Default |
On entry: if the argument settings in the call to e04nkc will be printed.
print_level – Nag_PrintType | | Default |
On entry: the level of results printout produced by
e04nkc. The following values are available:
|
No output. |
|
The final solution. |
|
One line of output for each iteration. |
|
A longer line of output for each iteration with more information (line exceeds 80 characters). |
|
The final solution and one line of output for each iteration. |
|
The final solution and one long line of output for each iteration (line exceeds 80 characters). |
|
As with the matrix statistics (initial status of rows and columns, number of elements, density, biggest and smallest elements, etc.), factors resulting from the scaling procedure (if or ; see below), basis factorization statistics and details of the initial basis resulting from the Crash procedure (if ). |
Details of each level of results printout are described in
Section 12.3.
Constraint:
, , , , , or .
outfile – const char[512] | | Default |
On entry: the name of the file to which results should be printed. If then the stdout stream is used.
print_fun – pointer to function | | Default NULL |
On entry: printing function defined by you; the prototype of
is
void (*print_fun)(const Nag_Search_State *st, Nag_Comm *comm);
See
Section 12.3.1 below for further details.
prob_name – const char | | Default: |
obj_name – const char | | Default: |
rhs_name – const char | | Default: |
range_name – const char | | Default: |
bnd_name – const char | | Default: |
On entry: these options contain the names associated with the so-called MPSX form of the problem. The arguments contain, respectively, the names of: the problem; the objective (or free) row; the constraint right-hand side; the ranges, and the bounds. They are used in the detailed output when optional parameter .
crnames – char ** | | Default NULL |
On entry: if
is not
NULL then it must point to an array of
character strings with maximum string length
, containing the names of the columns and rows (i.e., variables and constraints) of the problem. Thus,
contains the name of the
th column (variable), for
, and
contains the names of the
th row (constraint), for
. If supplied, the names are used in the solution output (see
Section 12.3).
minimize – Nag_Boolean | | Default |
On entry:
specifies the required direction of 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
and the other maximizes
, their solutions will be the same but the signs of the dual variables
and the reduced gradients
(see
Section 11.3) will be reversed.
max_iter – Integer | | Default |
On entry:
specifies the maximum number of iterations allowed before termination.
If you wish to check that a call to e04nkc is correct before attempting to solve the problem in full then may be set to . No iterations will then be performed but all initialization prior to the first iteration will be done and a listing of argument settings will be output, if optional parameter (the default setting).
Constraint:
.
crash – Nag_CrashType | | Default |
This option does not apply when optional parameter .
On entry: if
, and internal Crash procedure is used to select an initial basis from various rows and columns of the constraint matrix
. The value of
determines which rows and columns are initially eligible for the basis, and how many times the Crash procedure is called.
If
, the all-slack basis
is chosen.
- The Crash procedure is called once (looking for a triangular basis in all rows and columns of the linear constraint matrix ).
- The Crash procedure is called twice (looking at any equality constraints first followed by any inequality constraints).
If or , certain slacks on inequality rows are selected for the basis first. (If , numerical values are used to exclude slacks that are close to a bound.) The Crash procedure then makes several passes through the columns of , 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.
Constraint:
, or .
crash_tol – double | | Default |
On entry:
allows the Crash procedure to ignore certain ‘small’ nonzero elements in the constraint matrix
while searching for a triangular basis. For each column of
, if
is the largest element in the column, other nonzeros in that column are ignored if they are less than (or equal to)
.
When , 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 with more column variables and fewer (arbitrary) slacks. A feasible solution may be reached earlier for some problems.
Constraint:
.
scale – Nag_ScaleType | | Default |
On entry: this option enables the scaling of the variables and constraints using an iterative procedure due to
Fourer (1982), which attempts to compute row scales
and column scales
such that the scaled matrix coefficients
are as close as possible to unity. This may improve the overall efficiency of the function on some problems. (The lower and upper bounds on the variables and slacks for the scaled problem are redefined as
and
respectively, where
if
.)
- No scaling is performed.
- All rows and columns of the constraint matrix are scaled.
- An additional scaling is performed that may be helpful when the solution is large; it takes into account columns of that are fixed or have positive lower bounds or negative upper bounds.
Constraint:
, or .
scale_tol – double | | Default |
This option does not apply when optional parameter .
On entry: is used to control the number of scaling passes to be made through the constraint matrix . At least 3 (and at most 10) passes will be made. More precisely, let denote the largest column ratio (i.e., ('biggest' element)/('smallest' element) in some sense) after the th scaling pass through . The scaling procedure is terminated if for some . Thus, increasing the value of from to (say) will probably increase the number of passes through .
Constraint:
.
optim_tol – double | | Default |
On entry: is used to judge the size of the reduced gradients . By definition, the reduced gradients for basic variables are always zero. Optimality is declared if the reduced gradients for any nonbasic variables at their lower or upper bounds satisfy , and if for any superbasic variables.
Constraint:
.
ftol – double | | Default |
On entry:
defines the maximum acceptable
absolute violation in each constraint at a ‘feasible’ point (including slack variables). For example, if the variables and the coefficients in the linear constraints are of order unity, and the latter are correct to about 6 decimal digits, it would be appropriate to specify
as
.
e04nkc 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 by a factor of 10 or . Otherwise, some error in the data should be suspected. Note that e04nkc does not attempt to find the minimum value of Sinf.
If the constraints and variables have been scaled (see optional parameter above), then feasibility is defined in terms if the scaled problem (since it is more likely to be meaningful).
Constraint:
.
reset_ftol – Integer | | Default |
On entry: this option is part of an anti-cycling procedure designed to guarantee progress even on highly degenerate problems (see
Section 11.4).
For LP 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 parameter is . Over a period of iterations, the feasibility tolerance actually used by e04nkc (i.e., the working feasibility tolerance) increases from to (in steps of ).
For QP problems, the same procedure is used for iterations in which there is only one superbasic variable. (Cycling can only occur when the current solution is at a vertex of the feasible region.) Thus, zero steps are allowed if there is more than one superbasic variable, but otherwise positive steps are enforced.
Increasing the value of helps reduce the number of slightly infeasible nonbasic basic variables (most of which are eliminated during the resetting procedure). However, it also diminishes the freedom to choose a large pivot element (see below).
Constraint:
.
fcheck – Integer | | Default |
On entry: every th iteration after the most recent basis factorization, a numerical test is made to see if the current solution satisfies the linear constraints . If the largest element of the residual vector is judged to be too large, the current basis is refactorized and the basic variables recomputed to satisfy the constraints more accurately.
Constraint:
.
factor_freq – Integer | | Default |
On entry: at most basis changes will occur between factorizations of the basis matrix. For LP problems, the basis factors are usually updated at every iteration. 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 parameter to ensure that the linear constraints are satisfied. If necessary, the basis will be refactorized before the limit of updates is reached.
Constraint:
.
partial_price – Integer | | Default |
This option does not apply to QP problems.
On entry: this option is recommended for large FP or LP problems that have significantly more variables than constraints (i.e., ). It reduces the work required for each pricing operation (i.e., when a nonbasic variable is selected to enter the basis). If , all columns of the constraint matrix are searched. If , and are partitioned to give roughly equal segments , for (modulo ). If the previous pricing search was successful on , the next search begins on the segments . 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 , and so on.
Constraint:
.
pivot_tol – double | | Default |
On entry: is used to prevent columns entering the basis if they would cause the basis to become almost singular.
Constraint:
.
lu_factor_tol – double | | Default |
lu_update_tol – double | | Default |
On entry:
and
affect the stability and sparsity of the basis factorization
, during refactorization and updates respectively. The lower triangular matrix
is a product of matrices of the form
where the multipliers
will satisfy
during refactorization or
during update. The default values of
and
usually strike a good compromise between stability and sparsity. For large and relatively dense problems, setting
and
to 25 (say) may give a marked improvement in sparsity without impairing stability to a serious degree. Note that for band matrices it may be necessary to set
in the range
in order to achieve stability.
Constraints:
- ;
- .
lu_sing_tol – double | | Default |
On entry: defines the singularity tolerance used to guard against ill conditioned basis matrices. Whenever the basis is refactorized, the diagonal elements of are tested as follows. If or , the th column of the basis is replaced by the corresponding slack variable.
Constraint:
.
max_sb – Integer | | Default |
This option does not apply to FP or LP problems.
On entry:
places an upper bound on the number of variables which may enter the set of superbasic variables (see
Section 11.2). If the number of superbasics exceeds this bound then
e04nkc will terminate with
. In effect,
specifies ‘how nonlinear’ the QP problem is expected to be.
Constraint:
.
inf_bound – double | | Default |
On entry: defines the ‘infinite’ bound in the definition of the problem constraints. Any upper bound greater than or equal to will be regarded as (and similarly any lower bound less than or equal to will be regarded as ).
Constraint:
.
inf_step – double | | Default |
On entry: 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 during an iteration would exceed the value of , the objective function is considered to be unbounded below in the feasible region.
Constraint:
.
state – Integer * | | Default memory |
On entry:
need not be set if the default option of
is used as
values of memory will be automatically allocated by
e04nkc.
If the option
has been chosen,
must point to a minimum of
elements of memory. This memory will already be available if the
options structure has been used in a previous call to
e04nkc from the calling program, with
and the same values of
n and
m. If a previous call has not been made you must allocate sufficient memory.
If you supply a
vector and
, then the first
n elements of
must specify the initial states of the problem variables. (The slacks
need not be initialized.) An internal Crash procedure is then used to select an initial basis matrix
. The initial basis matrix will be triangular (neglecting certain small elements in each column). It is chosen from various rows and columns of
. Possible values for
, for
, are:
|
State of 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 (and ), for . All variables will then be eligible for the initial basis. Less trivially, to say that the th variable will probably be equal to one of its bounds, you should set and or and as appropriate.
Following the Crash procedure, variables for which are made superbasic. Other variables not selected for the basis are then made nonbasic at the value if , or at the value or closest to .
When
,
and
xs must specify the initial states and values, respectively, of the variables and slacks
. If
e04nkc has been called previously with the same values of
n and
m,
already contains satisfactory information.
Constraints:
- if , for ;
- if , for .
On exit: the final states of the variables and slacks
. The significance of each possible value of
is as follows:
|
State of variable |
Normal value of |
|
Nonbasic |
|
|
Nonbasic |
|
|
Superbasic |
Between and |
|
Basic |
Between and |
If the problem is feasible (i.e., ), basic and superbasic variables may be outside their bounds by as much as optional parameter . Note that unless the optional parameter , applies to the variables of the scaled problem. In this case, the variables of the original problem may be as much as 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 , and there may be some nonbasic variables for which lies strictly between its bounds.
If the problem is infeasible (i.e.,
), some basic and superbasic variables may be outside their bounds by an arbitrary amount (bounded by
sinf if
).
lambda – double * | | Default memory |
On entry: values of memory will be automatically allocated by e04nkc and this is the recommended method of use of . However you may supply memory from the calling program.
On exit: the values of the multipliers for each constraint with respect to the current working set. The first
n elements contain the multipliers (
reduced costs) for the bound constraints on the variables, and the next
m elements contain the Lagrange multipliers (
shadow prices) for the general linear constraints.
On entry: , the number of superbasics. For QP problems, need not be specified if optional parameter , but must retain its value from a previous call when . For FP and LP problems, is not referenced.
Constraint:
.
On exit: the final number of superbasics. This will be zero for FP and LP problems.
On exit: the total number of iterations performed.
On exit: the number of times the product
has been calculated (i.e., number of calls of
qphx).
12.3
Description of Printed Output
The level of printed output can be controlled with the structure members
and
(see
Section 12.2). If
then the argument values to
e04nkc are listed, whereas the printout of results is governed by the value of
. The default of
provides a single short line of output at each iteration and the final result. This section describes all of the possible levels of results printout available from
e04nkc.
When
or
the output produced at each iteration is as follows:
Itn |
is the iteration count. |
Step |
is the step taken along the computed search direction. |
Ninf |
is the number of violated constraints (infeasibilities). This will be zero during the optimality phase. |
Sinf/Objective |
is the current value of the objective function. If is not feasible, Sinf gives the sum of magnitudes of constraint violations. If is feasible, Objective is the value of the objective function. The output line for the final iteration of the feasibility phase (i.e., the first iteration for which Ninf is zero) will give the value of the true objective at the first feasible point. |
|
During the optimality phase, the value of the objective function will be non-increasing. During the feasibility phase, the number of constraint infeasibilities will not increase until either a feasible point is found, or the optimality of the multipliers implies that no feasible point exists. |
Norm rg |
is , the Euclidean norm of the reduced gradient (see Section 11.3). During the optimality phase, this norm will be approximately zero after a unit step. For FP and LP problems, Norm rg is not printed. |
In all cases, the values of the quantities printed are those in effect on completion of the given iteration.
If
,
or
the following, more detailed, line of output is produced at every iteration. In all cases, the values of the quantities printed are those in effect
on completion of the given iteration.
Itn |
is the iteration count. |
pp |
is the partial price indicator. The variable selected by the last pricing operation came from the ppth partition of and . 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. |
+S |
is the variable selected by the pricing operation to be added to the superbasic set. |
-S |
is the variable chosen to leave the superbasic set. |
-B |
is the variable removed from the basis (if any) to become nonbasic. |
-B |
is the variable chosen to leave the set of basics (if any) in a special basic superbasic swap. The entry under -S has become basic if this entry is nonzero, and nonbasic otherwise. The swap is done to ensure that there are no superbasic slacks. |
Step |
is the value of the steplength taken along the computed search direction . The variables have been changed to . If a variable is made superbasic during the current iteration (i.e., +S 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 th element of a vector satisfying whenever (the th column of the constraint matrix ) replaces the th column of the basis matrix . 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 parameter (default value , where is the machine precision; see Section 12.2) to exclude very small elements of from consideration during the computation of Step. |
Ninf |
is the number of violated constraints (infeasibilities). This will be zero during the optimality phase. |
Sinf/Objective |
is the current value of the objective function. If is not feasible, Sinf gives the sum of magnitudes of constraint violations. If is feasible, Objective is the value of the objective function. The output line for the final iteration of the feasibility phase (i.e., the first iteration for which Ninf is zero) will give the value of the true objective at the first feasible point. |
|
During the optimality phase, the value of the objective function will be non-increasing. During the feasibility phase, the number of constraint infeasibilities will not increase until either a feasible point is found, or the optimality of the multipliers implies that no feasible point exists. |
L |
is the number of nonzeros in the basis factor . Immediately after a basis factorization , this is lenL, the number of subdiagonal elements in the columns of a lower triangular matrix. Further nonzeros are added to L when various columns of are later replaced. (Thus, L increases monotonically.) |
U |
is the number of nonzeros in the basis factor . Immediately after a basis factorization, this is lenU, the number of diagonal and superdiagonal elements in the rows of an upper triangular matrix. As columns of are replaced, the matrix 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 . This includes the number of compressions needed during the previous basis factorization. Normally, Ncp should increase very slowly. If it does not, e04nkc will attempt to expand the internal workspace allocated for the basis factors. |
Norm rg |
is , the Euclidean norm of the reduced gradient (see Section 11.3). During the optimality phase, this norm will be approximately zero after a unit step. For FP and LP problems, Norm rg is not printed. |
Ns |
is the current number of superbasic variables. For FP and LP problems, Ns is not printed. |
Cond Hz |
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. For FP and LP problems, Cond Hz is not printed. |
When
the following intermediate printout (
characters) is produced whenever the matrix
or
is factorized. Gaussian elimination is used to compute an
factorization of
or
, where
is a lower triangular matrix and
is an upper triangular matrix for some permutation matrices
and
. The factorization is stabilized in the manner described under the optional parameter
(see
Section 12.2).
Factorize |
is the factorization count. |
Demand |
is a code giving the reason for the present factorization as follows:
Code |
Meaning |
|
First factorization. |
|
Number of updates reached the value of the optional parameter (see Section 12.2). |
|
Excessive nonzeros in updated factors. |
|
Not enough storage to update factors. |
|
Row residuals too large (see the description for the optional parameter in Section 12.2). |
|
Ill conditioning has caused inconsistent results. |
|
Iteration |
is the iteration count. |
Nonlinear |
is the number of nonlinear variables in (not printed if is factorized). |
Linear |
is the number of linear variables in (not printed if is factorized). |
Slacks |
is the number of slack variables in (not printed if is factorized). |
Elems |
is the number of nonzeros in (not printed if is factorized). |
Density |
is the percentage nonzero density of (not printed if is factorized). More precisely,
. |
Compressns |
is the number of times the data structure holding the partially factorized matrix needed to be compressed, in order to recover unused workspace. |
Merit |
is the average Markowitz merit count for the elements chosen to be the diagonals of . Each merit count is defined to be , where and 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 m such quantities. It gives an indication of how much work was required to preserve sparsity during the factorization. |
lenL |
is the number of nonzeros in . |
lenU |
is the number of nonzeros in . |
Increase |
is the percentage increase in the number of nonzeros in and relative to the number of nonzeros in . More precisely, . |
m |
is the number of rows in the problem. Note that . |
Ut |
is the number of triangular rows of at the top of . |
d1 |
is the number of columns remaining when the density of the basis matrix being factorized reached 0.3. |
Lmax |
is the maximum subdiagonal element in the columns of (not printed if is factorized). This will not exceed the value of the optional parameter . |
Bmax |
is the maximum nonzero element in (not printed if is factorized). |
BSmax |
is the maximum nonzero element in (not printed if is factorized). |
Umax |
is the maximum nonzero element in , excluding elements of that remain in unchanged. (For example, if a slack variable is in the basis, the corresponding row of will become a row of without modification. Elements in such rows will not contribute to Umax. If the basis is strictly triangular, none of the elements of will contribute, and Umax will be zero.) |
|
Ideally, Umax should not be significantly larger than Bmax. If it is several orders of magnitude larger, it may be advisable to reset the optional parameter to a value near . Umax is not printed if is factorized. |
Umin |
is the magnitude of the smallest diagonal element of (not printed if is factorized). |
Growth |
is the value of the ratio , which should not be too large. |
|
Providing Lmax is not large (say ), the ratio is an estimate of the condition number of . If this number is extremely large, the basis is nearly singular and some numerical difficulties could occur in subsequent computations. (However, an effort is made to avoid near singularity by using slacks to replace columns of that would have made Umin extremely small, and the modified basis is refactorized.) |
|
Growth is not printed if is factorized. |
Lt |
is the number of triangular columns of at the beginning of . |
bp |
is the size of the ‘bump’ or block to be factorized nontrivially after the triangular rows and columns have been removed. |
d2 |
is the number of columns remaining when the density of the basis matrix being factorized reached 0.6. |
When
the following lines of intermediate printout (
characters) are produced whenever
(see
Section 12.2). They refer to the number of columns selected by the Crash procedure during each of several passes through
, whilst searching for a triangular basis matrix.
Slacks |
is the number of slacks selected initially. |
Free Cols |
is the number of free columns in the basis. |
Preferred |
is the number of ‘preferred’ columns in the basis (i.e., for some ). |
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. |
When
the following lines of intermediate printout (
characters) are produced, following the final iteration. They refer to the ‘MPSX names’ stored in the optional parameters
,
,
,
and
(see
Section 12.2).
Name |
gives the name for the problem (blank if none). |
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)). |
Objective |
gives the name of the free row for the problem (blank if none). |
RHS |
gives the name of the constraint right-hand side for the problem (blank if none). |
Ranges |
gives the name of the ranges for the problem (blank if none). |
Bounds |
gives the name of the bounds for the problem (blank if none). |
When
or
final printout includes a listing of the status of every variable and constraint. The following describes the printout for each variable.
Variable |
gives the name of variable , for . If an options structure is supplied to e04nkc, and the member is assigned to an array of column and row names (see Section 12.2 for details), the name supplied in is assigned to the th variable. Otherwise, a default name is assigned to the variable. |
State |
gives the state of the variable (LL if nonbasic on its lower bound, UL if nonbasic on its upper bound, EQ if nonbasic and fixed, FR if nonbasic and strictly between its bounds, BS if basic and SBS if superbasic). |
|
A key is sometimes printed before State to give some additional information about the state of a variable. Note that unless the optional parameter (default value is ; see Section 12.2) 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 to (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 optional parameter (default value , where is the machine precision; see Section 12.2). |
N |
Not precisely optimal. The variable is nonbasic or superbasic. If the value of the reduced gradient for the variable exceeds the value of the optional parameter (default value ; see Section 12.2), the solution would not be declared optimal because the reduced gradient for the variable would not be considered negligible. |
|
Value |
is the value of the variable at the final iteration. |
Lower Bound |
is the lower bound specified for variable . (None indicates that , where is the optional parameter.) |
Upper Bound |
is the upper bound specified for variable . (None indicates that .) |
Lagr Mult |
is the value of the Lagrange multiplier for the associated bound. This will be zero if State is FR. If is optimal, the multiplier should be non-negative if State is LL, non-positive if State is UL, and zero if State is BS or SBS. |
Residual |
is the difference between the variable Value and the nearer of its (finite) bounds and . A blank entry indicates that the associated variable is not bounded (i.e., and ). |
The meaning of the printout for general constraints is the same as that given above for variables, with ‘variable’ replaced by ‘constraint’,
replaced by
,
replaced by
,
and
replaced by
and
respectively, and with the following change in the heading:
Constrnt |
gives the name of the linear constraint. |
Note that the 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.
When or , the following longer lines of final printout ( characters) are produced.
Let
denote the
th column of
, for
. The following describes the printout for each column (or variable).
Number |
is the column number . (This is used internally to refer to in the intermediate output.) |
Column |
gives the name of . |
State |
gives the state of (LL if nonbasic on its lower bound, UL if nonbasic on its upper bound, EQ if nonbasic and fixed, FR if nonbasic and strictly between its bounds, BS if basic and SBS if superbasic). |
|
A key is sometimes printed before State to give some additional information about the state of . Note that unless the optional parameter (default value is ; see Section 12.2) is specified, the tests for assigning a key are applied to the variables of the scaled problem.
A |
Alternative optimum possible. is nonbasic, but its reduced gradient is essentially zero. This means that if 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 basic and superbasic 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. is basic or superbasic, but it is equal to (or very close to) one of its bounds. |
I |
Infeasible. is basic or superbasic and is currently violating one of its bounds by more than the value of the optional parameter (default value , where is the machine precision; see Section 12.2). |
N |
Not precisely optimal. is nonbasic or superbasic. If the value of the reduced gradient for exceeds the value of the optional parameter (default value ; see Section 12.2), the solution would not be declared optimal because the reduced gradient for would not be considered negligible. |
|
Activity |
is the value of at the final iterate. |
Obj Gradient |
is the value of at the final iterate. For FP problems, is set to zero. |
Lower Bound |
is the lower bound specified for . (None indicates that , where is the optional parameter.) |
Upper Bound |
is the upper bound specified for . (None indicates that .) |
Reduced Gradnt |
is the value of at the final iterate (see Section 11.3). For FP problems, is set to zero. |
|
is the value of . |
Let
denote the
th row of
, for
. The following describes the printout for each row (or constraint).
Number |
is the value of . (This is used internally to refer to in the intermediate output.) |
Row |
gives the name of . |
State |
gives the state of (LL if active on its lower bound, UL if active on its upper bound, EQ if active and fixed, BS if inactive when is basic and SBS if inactive when is superbasic). |
|
A key is sometimes printed before State to give some additional information about the state of . Note that unless the optional parameter (default value is ; see Section 12.2) is specified, the tests for assigning a key are applied to the variables of the scaled problem.
A |
Alternative optimum possible. is nonbasic, but its reduced gradient is essentially zero. This means that if 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 basic and superbasic 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 dual variables (or Lagrange multipliers) might also change. |
D |
Degenerate. is basic or superbasic, but it is equal to (or very close to) one of its bounds. |
I |
Infeasible. is basic or superbasic and is currently violating one of its bounds by more than the value of the optional parameter (default value , where is the machine precision; see Section 12.2). |
N |
Not precisely optimal. is nonbasic or superbasic. If the value of the reduced gradient for exceeds the value of the optional parameter (default value ; see Section 12.2), the solution would not be declared optimal because the reduced gradient for would not be considered negligible. |
|
Activity |
is the value of at the final iterate. |
Slack Activity |
is the value by which differs from its nearest bound. (For the free row (if any), it is set to Activity.) |
Lower Bound |
is the lower bound specified for . None indicates that , where is the optional parameter. |
Upper Bound |
is the upper bound specified for . None indicates that . |
Dual Activity |
is the value of the dual variable (the Lagrange multiplier for ; see Section 11.3). For FP problems, is set to zero. |
i |
gives the index of . |
Numerical values are output with a fixed number of digits; they are not guaranteed to be accurate to this precision.
If then printout will be suppressed; you can print the final solution when e04nkc returns to the calling program.
12.3.1
Output of results via a user-defined printing function
You may also specify your own print function for output of iteration results and the final solution by use of the
function pointer, which has prototype
void (*print_fun)(const Nag_Search_State *st, Nag_Comm *comm);
The rest of this section can be skipped if you wish to use the default printing facilities.
When a user-defined function is assigned to
this will be called in preference to the internal print function of
e04nkc. Calls to the user-defined function are again controlled by means of the
member. Information is provided through
st and
comm, the two structure arguments to
.
If then the results from the last iteration of e04nkc are provided through st. Note that will be called with only if , , , or .
The following members of st are set:
- iter – Integer
-
The iteration count.
- qp – Nag_Boolean
-
Nag_TRUE if a QP problem is being solved; Nag_FALSE otherwise.
- pprice – Integer
-
The partial price indicator.
- rgval – double
-
The value of the reduced gradient (or reduced cost) for the variable selected by the pricing operation at the start of the current iteration.
- sb_add – Integer
-
The variable selected to enter the superbasic set.
- sb_leave – double
-
The variable chosen to leave the superbasic set.
- b_leave – Integer
-
The variable chosen to leave the basis (if any) to become nonbasic.
- bswap_leave – Integer
-
The variable chosen to leave the basis (if any) in a special basic superbasic swap.
- step – double
-
The step length taken along the computed search direction.
- pivot – double
-
The th element of a vector satisfying whenever (the th column of the constraint matrix ) replaces the th column of the basis matrix .
- ninf – Integer
-
The number of violated constraints or infeasibilities.
- f – double
-
The current value of the objective function if is zero; otherwise, the sum of the magnitudes of constraint violations.
- nnz_l – Integer
-
The number of nonzeros in the basis factor .
- nnz_u – Integer
-
The number of nonzeros in the basis factor .
- ncp – Integer
-
The number of compressions of the basis factorization workspace carried out so far.
- norm_rg – double
-
The Euclidean norm of the reduced gradient at the start of the current iteration. This value is meaningful only if .
- nsb – Integer
-
The number of superbasic variables. This value is meaningful only if .
- cond_hz – double
-
A lower bound on the condition number of the reduced Hessian. This value is meaningful only if .
If then the final results for one row or column are provided through st. Note that will be called with only if , , or . The following members of st are set (note that is called repeatedly, for each row and column):
- m – Integer
-
The number of rows (or general constraints) in the problem.
- n – Integer
-
The number of columns (or variables) in the problem.
- col – Nag_Boolean
-
Nag_TRUE if column information is being provided; Nag_FALSE if row information is being provided.
- index – Integer
-
If then is the index (in the range ) of the current column (variable) for which the remaining members of st, as described below, are set.
If then is the index (in the range i ) of the current row (constraint) for which the remaining members of st, as described below, are set.
- name – char *
-
The name of row or column .
- sstate – char *
-
is a character string describing the state of row
or column
. This may be
"LL",
"UL",
"EQ",
"FR",
"BS" or
"SBS". The meaning of each of these is described in
Section 12.3 (
State).
- key – char *
-
is a character string which gives additional information about the current row or column. The possible values of
are:
" ",
"A",
"D",
"I" or
"N". The meaning of each of these is described in
Section 12.3 (
State).
- val – double
-
The activity of row or column at the final iterate.
- blo – double
-
The lower bound on row or column .
- bup – double
-
The upper bound on row or column .
- lmult – double
-
The value of the Lagrange multiplier associated with the current row or column (i.e., the dual activity for a row, or the reduced gradient for a column) at the final iterate.
- objg – double
-
The value of the objective gradient at the final iterate. is meaningful only when and should not be accessed otherwise. It is set to zero for FP problems.
The relevant members of the structure
comm are:
- it_prt – Nag_Boolean
-
Will be Nag_TRUE when the print function is called with the result of the current iteration.
- sol_prt – Nag_Boolean
-
Will be Nag_TRUE when the print function is called with the final result.
- user – double
- iuser – Integer
- p – Pointer
-
Pointers for communication of user information. If used they must be allocated memory either before entry to e04nkc or during a call to qphess or . The type Pointer will be void * with a C compiler that defines void * and char * otherwise.