NAG FL Interface
e04uff
(nlp1_rcomm_old)
e04ufa (nlp1_rcomm)
Note: this routine uses optional parameters 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 parameters, 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 parameters and to Section 13 for a detailed description of the monitoring information produced by the routine.
1
Purpose
e04uff/e04ufa is designed to minimize an arbitrary smooth function subject to constraints (which may include simple bounds on the variables, linear constraints and smooth nonlinear constraints) using a sequential quadratic programming (SQP) method. You should supply as many first derivatives as possible; any unspecified derivatives are approximated by finite differences. It is not intended for large sparse problems.
e04uff/e04ufa may also be used for unconstrained, bound-constrained and linearly constrained optimization.
e04uff/e04ufa uses reverse communication for evaluating the objective function, the nonlinear constraint functions and any of their derivatives.
e04ufa is a version of
e04uff that has additional arguments in order to make it safe for use in multithreaded applications (see
Section 5). The initialization routine
e04wbf must have been called before calling
e04ufa.
2
Specification
2.1
Specification for e04uff
Fortran Interface
Subroutine e04uff ( |
irevcm, n, nclin, ncnln, lda, ldcj, ldr, a, bl, bu, iter, istate, c, cjac, clamda, objf, objgrd, r, x, needc, iwork, liwork, work, lwork, ifail) |
Integer, Intent (In) |
:: |
n, nclin, ncnln, lda, ldcj, ldr, liwork, lwork |
Integer, Intent (Inout) |
:: |
irevcm, iter, istate(n+nclin+ncnln), iwork(liwork), ifail |
Integer, Intent (Out) |
:: |
needc(max(1,ncnln)) |
Real (Kind=nag_wp), Intent (In) |
:: |
a(lda,*), bl(n+nclin+ncnln), bu(n+nclin+ncnln) |
Real (Kind=nag_wp), Intent (Inout) |
:: |
c(*), cjac(ldcj,*), clamda(n+nclin+ncnln), objf, objgrd(n), r(ldr,n), x(n), work(lwork) |
|
C Header Interface
#include <nag.h>
void |
e04uff_ (Integer *irevcm, const Integer *n, const Integer *nclin, const Integer *ncnln, const Integer *lda, const Integer *ldcj, const Integer *ldr, const double a[], const double bl[], const double bu[], Integer *iter, Integer istate[], double c[], double cjac[], double clamda[], double *objf, double objgrd[], double r[], double x[], Integer needc[], Integer iwork[], const Integer *liwork, double work[], const Integer *lwork, Integer *ifail) |
|
C++ Header Interface
#include <nag.h> extern "C" {
void |
e04uff_ (Integer &irevcm, const Integer &n, const Integer &nclin, const Integer &ncnln, const Integer &lda, const Integer &ldcj, const Integer &ldr, const double a[], const double bl[], const double bu[], Integer &iter, Integer istate[], double c[], double cjac[], double clamda[], double &objf, double objgrd[], double r[], double x[], Integer needc[], Integer iwork[], const Integer &liwork, double work[], const Integer &lwork, Integer &ifail) |
}
|
2.2
Specification for e04ufa
Fortran Interface
Subroutine e04ufa ( |
irevcm, n, nclin, ncnln, lda, ldcj, ldr, a, bl, bu, iter, istate, c, cjac, clamda, objf, objgrd, r, x, needc, iwork, liwork, work, lwork, cwsav, lwsav, iwsav, rwsav, ifail) |
Integer, Intent (In) |
:: |
n, nclin, ncnln, lda, ldcj, ldr, liwork, lwork |
Integer, Intent (Inout) |
:: |
irevcm, iter, istate(n+nclin+ncnln), iwork(liwork), iwsav(610), ifail |
Integer, Intent (Out) |
:: |
needc(max(1,ncnln)) |
Real (Kind=nag_wp), Intent (In) |
:: |
a(lda,*), bl(n+nclin+ncnln), bu(n+nclin+ncnln) |
Real (Kind=nag_wp), Intent (Inout) |
:: |
c(*), cjac(ldcj,*), clamda(n+nclin+ncnln), objf, objgrd(n), r(ldr,n), x(n), work(lwork), rwsav(475) |
Logical, Intent (Inout) |
:: |
lwsav(120) |
Character (80), Intent (InOut) |
:: |
cwsav(5) |
|
C Header Interface
#include <nag.h>
void |
e04ufa_ (Integer *irevcm, const Integer *n, const Integer *nclin, const Integer *ncnln, const Integer *lda, const Integer *ldcj, const Integer *ldr, const double a[], const double bl[], const double bu[], Integer *iter, Integer istate[], double c[], double cjac[], double clamda[], double *objf, double objgrd[], double r[], double x[], Integer needc[], Integer iwork[], const Integer *liwork, double work[], const Integer *lwork, char cwsav[], logical lwsav[], Integer iwsav[], double rwsav[], Integer *ifail, const Charlen length_cwsav) |
|
C++ Header Interface
#include <nag.h> extern "C" {
void |
e04ufa_ (Integer &irevcm, const Integer &n, const Integer &nclin, const Integer &ncnln, const Integer &lda, const Integer &ldcj, const Integer &ldr, const double a[], const double bl[], const double bu[], Integer &iter, Integer istate[], double c[], double cjac[], double clamda[], double &objf, double objgrd[], double r[], double x[], Integer needc[], Integer iwork[], const Integer &liwork, double work[], const Integer &lwork, char cwsav[], logical lwsav[], Integer iwsav[], double rwsav[], Integer &ifail, const Charlen length_cwsav) |
}
|
Before calling
e04ufa, or either of the option setting routines
e04uda or
e04uea,
e04wbf must be called. The specification for
e04wbf is:
Fortran Interface
Integer, Intent (In) |
:: |
lcwsav, llwsav, liwsav, lrwsav |
Integer, Intent (Inout) |
:: |
ifail |
Integer, Intent (Out) |
:: |
iwsav(liwsav) |
Real (Kind=nag_wp), Intent (Out) |
:: |
rwsav(lrwsav) |
Logical, Intent (Out) |
:: |
lwsav(llwsav) |
Character (*), Intent (In) |
:: |
rname |
Character (80), Intent (Out) |
:: |
cwsav(lcwsav) |
|
C Header Interface
#include <nag.h>
void |
e04wbf_ (const char *rname, char cwsav[], const Integer *lcwsav, logical lwsav[], const Integer *llwsav, Integer iwsav[], const Integer *liwsav, double rwsav[], const Integer *lrwsav, Integer *ifail, const Charlen length_rname, const Charlen length_cwsav) |
|
C++ Header Interface
#include <nag.h> extern "C" {
void |
e04wbf_ (const char *rname, char cwsav[], const Integer &lcwsav, logical lwsav[], const Integer &llwsav, Integer iwsav[], const Integer &liwsav, double rwsav[], const Integer &lrwsav, Integer &ifail, const Charlen length_rname, const Charlen length_cwsav) |
}
|
e04wbf should be called with
.
cwsav must have a length of
lcwsav
while the lengths of
lwsav,
iwsav and
rwsav should be
llwsav,
liwsav and
lrwsav, respectively. These arguments must satisfy the following constraints:
The contents of the arrays
cwsav,
lwsav,
iwsav and
rwsav
must not be altered between calling routines
e04uda,
e04uea,
e04ufa or
e04wbf.
3
Description
e04uff/e04ufa is designed to solve the nonlinear programming problem – the minimization of a smooth nonlinear function subject to a set of constraints on the variables. The problem is assumed to be stated in the following form:
where
(the
objective function) is a nonlinear function,
is an
by
constant matrix, and
is an
element vector of nonlinear constraint functions. (The matrix
and the vector
may be empty.) The objective function and the constraint functions are assumed to be smooth, i.e., at least twice-continuously differentiable. (The method of
e04uff/e04ufa will usually solve
(1) if there are only isolated discontinuities away from the solution.)
Note that although the bounds on the variables could be included in the definition of the linear constraints, we prefer to distinguish between them for reasons of computational efficiency. For the same reason, the linear constraints should
not be included in the definition of the nonlinear constraints. Upper and lower bounds are specified for all the variables and for all the constraints. An
equality constraint can be specified by setting
. If certain bounds are not present, the associated elements of
or
can be set to special values that will be treated as
or
. (See the description of the optional parameter
Infinite Bound Size.)
If there are no nonlinear constraints in
(1) and
is linear or quadratic then it will generally be more efficient to use one of
e04mff/e04mfa,
e04ncf/e04nca or
e04nff/e04nfa, or
e04nqf if the problem is large and sparse. If the problem is large and sparse and does have nonlinear constraints,
e04ugf/e04uga should be used, since
e04uff/e04ufa treats all matrices as dense.
e04uff/e04ufa uses reverse communication for evaluating
,
and as many of their first partial derivatives as possible; any remaining derivatives are approximated by finite differences. See the description of the optional parameter
Derivative Level.
On initial entry, you must supply an initial estimate of the solution to
(1).
On intermediate exits, the calling program must compute appropriate values for the objective function, the nonlinear constraints or their derivatives, as specified by the argument
irevcm, and then re-enter the routine.
For maximum reliability, it is preferable to provide all partial derivatives (see Chapter 8 of
Gill et al. (1981), for a detailed discussion). If they cannot all be provided, it is advisable to provide as many as possible. While developing code to evaluate the objective function and the constraints, the optional parameter
Verify should be used to check the calculation of any known derivatives.
The method used by
e04uff/e04ufa is described in detail in
Section 11.
e04wdf is an alternative routine which uses a similar method, but with
direct communication:
that is, the objective and constraint functions are evaluated by subroutines, supplied as arguments to the routine.
4
References
Dennis J E Jr and Moré J J (1977) Quasi-Newton methods, motivation and theory SIAM Rev. 19 46–89
Dennis J E Jr and Schnabel R B (1981) A new derivation of symmetric positive-definite secant updates nonlinear programming (eds O L Mangasarian, R R Meyer and S M Robinson) 4 167–199 Academic Press
Dennis J E Jr and Schnabel R B (1983) Numerical Methods for Unconstrained Optimization and Nonlinear Equations Prentice–Hall
Fletcher R (1987) Practical Methods of Optimization (2nd Edition) Wiley
Gill P E, Hammarling S, Murray W, Saunders M A and Wright M H (1986) Users' guide for LSSOL (Version 1.0) Report SOL 86-1 Department of Operations Research, Stanford University
Gill P E, Murray W, Saunders M A and Wright M H (1984a) Procedures for optimization problems with a mixture of bounds and general linear constraints ACM Trans. Math. Software 10 282–298
Gill P E, Murray W, Saunders M A and Wright M H (1984b) Users' guide for SOL/QPSOL version 3.2 Report SOL 84–5 Department of Operations Research, Stanford University
Gill P E, Murray W, Saunders M A and Wright M H (1986a) Some theoretical properties of an augmented Lagrangian merit function Report SOL 86–6R Department of Operations Research, Stanford University
Gill P E, Murray W, Saunders M A and Wright M H (1986b) Users' guide for NPSOL (Version 4.0): a Fortran package for nonlinear programming Report SOL 86-2 Department of Operations Research, Stanford University
Gill P E, Murray W and Wright M H (1981) Practical Optimization Academic Press
Hock W and Schittkowski K (1981) Test Examples for Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical Systems 187 Springer–Verlag
Murtagh B A and Saunders M A (1983) MINOS 5.0 user's guide Report SOL 83-20 Department of Operations Research, Stanford University
Powell M J D (1974) Introduction to constrained optimization Numerical Methods for Constrained Optimization (eds P E Gill and W Murray) 1–28 Academic Press
Powell M J D (1983) Variable metric methods in constrained optimization Mathematical Programming: the State of the Art (eds A Bachem, M Grötschel and B Korte) 288–311 Springer–Verlag
5
Arguments
Note: this routine uses
reverse communication. Its use involves an initial entry, intermediate exits and re-entries, and a final exit, as indicated by the argument
irevcm. Between intermediate exits and re-entries,
all arguments other than those specified by the value of irevcm must remain unchanged.
-
1:
– Integer
Input/Output
-
On initial entry: must be set to .
On intermediate exit:
specifies what values the calling program must assign to arguments of
e04uff/e04ufa before re-entering the routine.
- Set objf to the value of the objective function .
- Set
to the value if available, for .
- Set objf and as for and .
- Set to the value of the constraint function , for each such that .
- Set to the value if available, for each such that and .
- Set and as for and .
On intermediate re-entry:
must remain unchanged, unless you wish to terminate the solution to the current problem. In this case
irevcm may be set to a negative value and then
e04uff/e04ufa will take a final exit with
ifail set to this value of
irevcm.
On final exit: .
Constraint:
.
Note: any values you return to e04uff/e04ufa as part of the reverse communication procedure should not include floating-point NaN (Not a Number) or infinity values, since these are not handled by e04uff/e04ufa. If your code does inadvertently return any NaNs or infinities, e04uff/e04ufa is likely to produce unexpected results.
-
2:
– Integer
Input
-
On initial entry: , the number of variables.
Constraint:
.
-
3:
– Integer
Input
-
On initial entry: , the number of general linear constraints.
Constraint:
.
-
4:
– Integer
Input
-
On initial entry: , the number of nonlinear constraints.
Constraint:
.
-
5:
– Integer
Input
-
On initial entry: the first dimension of the array
a as declared in the (sub)program from which
e04uff/e04ufa is called.
Constraint:
.
-
6:
– Integer
Input
-
On initial entry: the first dimension of the array
cjac as declared in the (sub)program from which
e04uff/e04ufa is called.
Constraint:
.
-
7:
– Integer
Input
-
On initial entry: the first dimension of the array
r as declared in the (sub)program from which
e04uff/e04ufa is called.
Constraint:
.
-
8:
– Real (Kind=nag_wp) array
Input
-
Note: the second dimension of the array
a
must be at least
if
, and at least
otherwise.
On initial entry: the
th row of the matrix
of general linear constraints in
(1) must be stored in
, for
and
.
That is, the
th row contains the coefficients of the
th general linear constraint, for
.
If
, the array
a is not referenced.
-
9:
– Real (Kind=nag_wp) array
Input
-
10:
– Real (Kind=nag_wp) array
Input
-
On initial 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, the next
elements the bounds for the general linear constraints (if any) and the next
elements the bounds for the general nonlinear constraints (if any). To specify a nonexistent lower bound (i.e.,
), set
, and to specify a nonexistent upper bound (i.e.,
), set
; the default value of
is
, but this may be changed by the optional parameter
Infinite Bound Size. To specify the
th constraint as an
equality, set
, say, where
.
Constraints:
- , for ;
- if , .
-
11:
– Integer
Input/Output
-
On intermediate re-entry: must remain unchanged from a previous call to e04uff/e04ufa.
On final exit: the number of major iterations performed.
-
12:
– Integer array
Input/Output
-
On initial entry: need not be set if the (default) optional parameter
Cold Start is used.
If the optional parameter
Warm Start has been chosen, the elements of
istate corresponding to the bounds and linear constraints define the initial working set for the procedure that finds a feasible point for the linear constraints and bounds. The active set at the conclusion of this procedure and the elements of
istate corresponding to nonlinear constraints then define the initial working set for the first QP subproblem. More precisely, the first
elements of
istate refer to the upper and lower bounds on the variables, the next
elements refer to the upper and lower bounds on
, and the next
elements refer to the upper and lower bounds on
. Possible values for
are as follows:
| Meaning |
0 | The corresponding constraint is not in the initial QP working set. |
1 | This inequality constraint should be in the working set at its lower bound. |
2 | This inequality constraint should be in the working set at its upper bound. |
3 | This equality constraint should be in the initial working set. This value must not be specified unless . |
The values
,
and
are also acceptable but will be modified by the routine. If
e04uff/e04ufa has been called previously with the same values of
n,
nclin and
ncnln,
istate already contains satisfactory information. (See also the description of the optional parameter
Warm Start.) The routine also adjusts (if necessary) the values supplied in
x to be consistent with
istate.
Constraint:
, for .
On final exit: the status of the constraints in the QP working set at the point returned in
x. The significance of each possible value of
is as follows:
| Meaning |
| This constraint violates its lower bound by more than the appropriate feasibility tolerance (see the optional parameters Linear Feasibility Tolerance and Nonlinear Feasibility Tolerance). This value can occur only when no feasible point can be found for a QP subproblem. |
| This constraint violates its upper bound by more than the appropriate feasibility tolerance (see the optional parameters Linear Feasibility Tolerance and Nonlinear Feasibility Tolerance). This value can occur only when no feasible point can be found for a QP subproblem. |
| The constraint is satisfied to within the feasibility tolerance, but is not in the QP working set. |
| This inequality constraint is included in the QP working set at its lower bound. |
| This inequality constraint is included in the QP working set at its upper bound. |
| This constraint is included in the QP working set as an equality. This value of istate can occur only when . |
-
13:
– Real (Kind=nag_wp) array
Input/Output
-
Note: the dimension of the array
c
must be at least
.
On initial entry: need not be set.
On intermediate re-entry: if
or
and
,
must contain the value of the
th constraint at
. The remaining elements of
c, corresponding to the non-positive elements of
needc, are ignored.
On final exit: if
,
contains the value of the
th nonlinear constraint function
at the final iterate, for
.
If
, the array
c is not referenced.
-
14:
– Real (Kind=nag_wp) array
Input/Output
-
Note: the second dimension of the array
cjac
must be at least
if
, and at least
otherwise.
On initial entry: in general,
cjac need not be initialized before the call to
e04uff/e04ufa. However, if the optional parameter
or
, you may optionally set the constant elements of
cjac. Such constant elements need not be re-assigned on subsequent intermediate exits.
If all elements of the constraint Jacobian are known (i.e.,
or
), any constant elements may be assigned to
cjac one time only at the start of the optimization. An element of
cjac that is not subsequently assigned during an intermediate exit will retain its initial value throughout. Constant elements may be loaded into
cjac either before the call to
e04uff/e04ufa or during the first intermediate exit. The ability to preload constants is useful when many Jacobian elements are identically zero, in which case
cjac may be initialized to zero and nonzero elements may be reset during intermediate exits.
On intermediate re-entry: if
or
and
, the
th row of
cjac must contain the available elements of the vector
given by
where
is the partial derivative of the
th constraint with respect to the
th variable, evaluated at the point
. The remaining rows of
cjac, corresponding to non-positive elements of
needc, are ignored. The
th row of the Jacobian should be stored in elements
, for
and
.
Note that constant nonzero elements do affect the values of the constraints. Thus, if is set to a constant value, it need not be reset during subsequent intermediate exits, but the value must nonetheless be added to . For example, if and , the term must be included in the definition of .
It must be emphasized that, if
or
, unassigned elements of
cjac are not treated as constant; they are estimated by finite differences, at nontrivial expense. If you do not supply a value for the optional parameter
Difference Interval, an interval for each element of
is computed automatically at the start of the optimization. The automatic procedure can usually identify constant elements of
cjac, which are then computed once only by finite differences.
See also the description of the optional parameter
Verify.
On final exit: if
,
cjac contains the Jacobian matrix of the nonlinear constraint functions at the final iterate, i.e.,
contains the partial derivative of the
th constraint function with respect to the
th variable, for
and
.
If
, the array
cjac is not referenced.
-
15:
– Real (Kind=nag_wp) array
Input/Output
-
On initial entry: need not be set if the (default) optional parameter
Cold Start is used.
If the optional parameter
Warm Start has been chosen,
must contain a multiplier estimate for each nonlinear constraint with a sign that matches the status of the constraint specified by the
istate array, for
. The remaining elements need not be set. Note that if the
th constraint is defined as ‘inactive’ by the initial value of the
istate array (i.e.
),
should be zero; if the
th constraint is an inequality active at its lower bound (i.e.
),
should be non-negative; if the
th constraint is an inequality active at its upper bound (i.e.
),
should be non-positive. If necessary, the routine will modify
clamda to match these rules.
On final exit: the values of the QP multipliers from the last QP subproblem. should be non-negative if and non-positive if .
-
16:
– Real (Kind=nag_wp)
Input/Output
-
On initial entry: need not be set.
On intermediate re-entry: if
or
,
objf must be set to the value of the objective function at
.
On final exit: the value of the objective function at the final iterate.
-
17:
– Real (Kind=nag_wp) array
Input/Output
-
On initial entry: need not be set.
On intermediate re-entry: if
or
,
objgrd must contain the available elements of the gradient evaluated at
.
See also the description of the optional parameter
Verify.
On final exit: the gradient of the objective function at the final iterate (or its finite difference approximation).
-
18:
– Real (Kind=nag_wp) array
Input/Output
-
On initial entry: need not be initialized if the (default) optional parameter
Cold Start is used.
If the optional parameter
Warm Start has been chosen,
r must contain the upper triangular Cholesky factor
of the initial approximation of the Hessian of the Lagrangian function, with the variables in the natural order. Elements not in the upper triangular part of
r are assumed to be zero and need not be assigned.
On final exit: if
,
r contains the upper triangular Cholesky factor
of
, an estimate of the transformed and reordered Hessian of the Lagrangian at
(see
(6) in
Section 11.1).
If
,
r contains the upper triangular Cholesky factor
of
, the approximate (untransformed) Hessian of the Lagrangian, with the variables in the natural order.
-
19:
– Real (Kind=nag_wp) array
Input/Output
-
On initial entry: an initial estimate of the solution.
On intermediate exit:
the point at which the objective function, constraint functions or their derivatives are to be evaluated.
On final exit: the final estimate of the solution.
-
20:
– Integer array
Output
-
On intermediate exit:
if
,
needc specifies the indices of the elements of
c and/or
cjac that must be assigned. If
, the
th element of
c and/or the available elements of the
th row of
cjac must be evaluated at
.
-
21:
– Integer array
Communication Array
-
22:
– Integer
Input
-
On initial entry: the dimension of the array
iwork as declared in the (sub)program from which
e04uff/e04ufa is called.
Constraint:
.
-
23:
– Real (Kind=nag_wp) array
Communication Array
-
24:
– Integer
Input
-
On initial entry: the dimension of the array
work as declared in the (sub)program from which
e04uff/e04ufa is called.
Constraints:
- if and , ;
- if and , ;
- if and , .
The amounts of workspace provided and required may be (by default for
e04uff) output on the current advisory message unit (as defined by
x04abf). As an alternative to computing
liwork and
lwork from the formulae given above, you may prefer to obtain appropriate values from the output of a preliminary run with
liwork and
lwork set to
. (
e04uff/e04ufa will then terminate with
.)
-
25:
– Integer
Input/Output
-
Note: for e04ufa, ifail does not occur in this position in the argument list. See the additional arguments described below.
On initial entry:
ifail must be set to
,
. If you are unfamiliar with this argument you should refer to
Section 4 in the Introduction to the NAG Library FL Interface for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
is recommended. If the output of error messages is undesirable, then the value
is recommended. Otherwise, because for this routine the values of the output arguments may be useful even if
on exit, the recommended value is
.
When the value is used it is essential to test the value of ifail on exit.
On final exit:
unless the routine detects an error or a warning has been flagged (see
Section 6).
e04uff/e04ufa returns with
if the iterates have converged to a point
that satisfies the first-order Kuhn–Tucker conditions (see
Section 11.1) to the accuracy requested by the optional parameter
Optimality Tolerance. This has
, where
is the value of the optional parameter
Function Precision (
, where
is the
machine precision). That is
when the projected gradient and active constraint residuals are negligible at
.
You should check whether the following four conditions are satisfied:
-
(i)the final value of Norm Gz (see Section 9.1) is significantly less than that at the starting point;
-
(ii)during the final major iterations, the values of Step and Mnr (see Section 9.1) are both one;
-
(iii)the last few values of both Norm Gz and Violtn (see Section 9.1) become small at a fast linear rate; and
-
(iv)Cond Hz (see Section 9.1) is small.
If all these conditions hold,
is almost certainly a local minimum of
(1).
- Note: the following are additional arguments for specific use with e04ufa. Users of e04uff therefore need not read the remainder of this description.
-
25:
– Character(80) array
Communication Array
-
26:
– Logical array
Communication Array
-
27:
– Integer array
Communication Array
-
28:
– Real (Kind=nag_wp) array
Communication Array
-
The arrays
lwsav,
iwsav,
rwsav and
cwsav must not be altered between calls to any of the routines
e04wbf,
e04ufa,
e04uda or
e04uea.
-
29:
– Integer
Input/Output
-
Note: see the argument description for
ifail above.
6
Error Indicators and Warnings
If on entry
or
, explanatory error messages are output on the current error message unit (as defined by
x04aaf).
Note: e04uff/e04ufa may return useful information for one or more of the following detected errors or warnings.
Errors or warnings detected by the routine:
-
Optimal solution found, but requested accuracy not achieved.
The final iterate
satisfies the first-order Kuhn–Tucker conditions (see
Section 11.1) to the accuracy requested, but the sequence of iterates has not yet converged.
e04uff/e04ufa was terminated because no further improvement could be made in the merit function (see
Section 9.1).
The most common situation is that you ask for a solution with accuracy that is not attainable with the given precision of the problem (as specified by the optional parameter
Function Precision). This condition will also occur if, by chance, an iterate is an ‘exact’ Kuhn–Tucker point, but the change in the variables was significant at the previous iteration. (This situation often happens when minimizing very simple functions, such as quadratics.)
-
No feasible point for the linear constraints.
e04uff/e04ufa has terminated without finding a feasible point for the linear constraints and bounds, which means that either no feasible point exists for the given value of the optional parameter
Linear Feasibility Tolerance, or no feasible point could be found in the number of iterations specified by the optional parameter
Minor Iteration Limit. You should check that there are no constraint redundancies. If the data for the constraints are accurate only to an absolute precision
, you should ensure that the value of the optional parameter
Linear Feasibility Tolerance is greater than
. For example, if all elements of
are of order unity and are accurate to only three decimal places,
Linear Feasibility Tolerance should be at least
.
-
No feasible point for the nonlinear constraints.
No feasible point could be found for the nonlinear constraints. The problem may have no feasible solution. This means that there has been a sequence of QP subproblems for which no feasible point could be found (indicated by
I at the end of each line of intermediate printout produced by the major iterations; see
Section 9.1). This behaviour will occur if there is no feasible point for the nonlinear constraints. (However, there is no general test that can determine whether a feasible point exists for a set of nonlinear constraints.) If the infeasible subproblems occur from the very first major iteration, it is highly likely that no feasible point exists. If infeasibilities occur when earlier subproblems have been feasible, small constraint inconsistencies may be present. You should check the validity of constraints with negative values of
istate. If you are convinced that a feasible point does exist,
e04uff/e04ufa should be restarted at a different starting point.
-
Too many major iterations.
The limiting number of iterations (as determined by the optional parameter
Major Iteration Limit) has been reached.
If the algorithm appears to be making satisfactory progress, optional parameter
Major Iteration Limit may be too small. If so, either increase its value and rerun
e04uff/e04ufa or, alternatively, rerun
e04uff/e04ufa using the optional parameter
Warm Start. If the algorithm seems to be making little or no progress however, you should check for incorrect gradients or ill-conditioning as described under
.
Note that ill-conditioning in the working set is sometimes resolved automatically by the algorithm, in which case performing additional iterations may be helpful. However, ill-conditioning in the Hessian approximation tends to persist once it has begun, so that allowing additional iterations without altering
is usually inadvisable. If the quasi-Newton update of the Hessian approximation was reset during the latter major iterations (i.e., an
R occurs at the end of each line of intermediate printout; see
Section 9.1), it may be worthwhile to try a
Warm Start at the final point as suggested above.
-
Current point cannot be improved upon.
does not satisfy the first-order Kuhn–Tucker conditions (see
Section 11.1), and no improved point for the merit function (see
Section 9.1) could be found during the final linesearch.
This sometimes occurs because an overly stringent accuracy has been requested, i.e., the value of the optional parameter
Optimality Tolerance (, where
is the value of the optional parameter
Function Precision) is too small. In this case you should apply the four tests described under
to determine whether or not the final solution is acceptable (see
Gill et al. (1981), for a discussion of the attainable accuracy).
If many iterations have occurred in which essentially no progress has been made and
e04uff/e04ufa has failed completely to move from the initial point, values set by the calling program for the objective or constraint functions or their derivatives during intermediate exits may be incorrect. You should refer to comments under
and check the gradients using the optional parameter
Verify. Unfortunately, there may be small errors in the objective and constraint gradients that cannot be detected by the verification process. Finite difference approximations to first derivatives are catastrophically affected by even small inaccuracies. An indication of this situation is a dramatic alteration in the iterates if the finite difference interval is altered. One might also suspect this type of error if a switch is made to central differences even when
Norm Gz and
Violtn (see
Section 9.1) are large.
Another possibility is that the search direction has become inaccurate because of ill-conditioning in the Hessian approximation or the matrix of constraints in the working set; either form of ill-conditioning tends to be reflected in large values of
Mnr (the number of iterations required to solve each QP subproblem; see
Section 9.1).
If the condition estimate of the projected Hessian (
Cond Hz; see
Section 9.1) is extremely large, it may be worthwhile rerunning
e04uff/e04ufa from the final point with the optional parameter
Warm Start. In this situation,
istate and
clamda should be left unaltered and
should be reset to the identity matrix.
If the matrix of constraints in the working set is ill-conditioned (i.e.,
Cond T is extremely large; see
Section 13), it may be helpful to run
e04uff/e04ufa with a relaxed value of the optional parameter
Feasibility Tolerance. (Constraint dependencies are often indicated by wide variations in size in the diagonal elements of the matrix
, whose diagonals will be printed if
.)
-
Large errors found in the derivatives.
The user-supplied derivatives of the objective function and/or nonlinear constraints appear to be incorrect.
Large errors were found in the derivatives of the objective function and/or nonlinear constraints. This value of
ifail will occur if the verification process indicated that at least one gradient or Jacobian element had no correct figures. You should refer to the printed output to determine which elements are suspected to be in error.
As a first-step, you should check that the code for the objective and constraint values is correct – for example, by computing the function at a point where the correct value is known. However, care should be taken that the chosen point fully tests the evaluation of the function. It is remarkable how often the values or are used in such a test, and how often the special properties of these numbers make the test meaningless.
Gradient checking will be ineffective if the objective function uses information computed by the constraints, since they are not necessarily computed before each function evaluation.
Errors in programming the function may be quite subtle in that the function value is ‘almost’ correct. For example, the function may not be accurate to full precision because of the inaccurate calculation of a subsidiary quantity, or the limited accuracy of data upon which the function depends. A common error on machines where numerical calculations are usually performed in double precision is to include even one single precision constant in the calculation of the function; since some compilers do not convert such constants to double precision, half the correct figures may be lost by such a seemingly trivial error.
-
Not enough workspace to solve problem. Workspace provided is and . To solve problem we need and .
On entry, .
Constraint: .
On entry, and .
Constraint: .
On entry, and .
Constraint: .
On entry, and .
Constraint: .
On entry, .
Constraint: .
On entry, .
Constraint: .
On entry, .
Constraint: .
On entry, the bounds on are inconsistent: and .
On entry, the bounds on linear constraint are inconsistent: and .
On entry, the bounds on nonlinear constraint are inconsistent: and .
On entry, the bounds on variable are inconsistent: and .
On entry, the equal bounds on are infinite, because and , but : and .
On entry, the equal bounds on linear constraint are infinite, because and , but : and .
On entry, the equal bounds on nonlinear constraint are infinite, because and , but : and .
On entry, the equal bounds on variable are infinite, because and , but : and .
On entry with a Warm Start, .
-
User requested termination by setting
irevcm negative during an intermediate exit.
- Overflow
If the printed output before the overflow error contains a warning about serious ill-conditioning in the working set when adding the
th constraint, it may be possible to avoid the difficulty by increasing the magnitude of the
Linear Feasibility Tolerance and/or the optional parameter
Nonlinear Feasibility Tolerance and rerunning the program. If the message recurs even after this change, the offending linearly dependent constraint (with index ‘
’) must be removed from the problem. If overflow occurs in one of the user-supplied subroutines (e.g., if the nonlinear functions involve exponentials or singularities), it may help to specify tighter bounds for some of the variables (i.e., reduce the gap between the appropriate
and
).
An unexpected error has been triggered by this routine. Please
contact
NAG.
See
Section 7 in the Introduction to the NAG Library FL Interface for further information.
Your licence key may have expired or may not have been installed correctly.
See
Section 8 in the Introduction to the NAG Library FL Interface for further information.
Dynamic memory allocation failed.
See
Section 9 in the Introduction to the NAG Library FL Interface for further information.
7
Accuracy
If
on final exit then the vector returned in the array
x is an estimate of the solution to an accuracy of approximately
Optimality Tolerance (
, where
is the
machine precision).
8
Parallelism and Performance
e04uff/e04ufa is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
e04uff/e04ufa 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
X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the
Users' Note for your implementation for any additional implementation-specific information.
9.1
Description of the Printed Output
This section describes the intermediate printout and final printout produced by
e04uff/e04ufa. The intermediate printout is a subset of the monitoring information produced by
e04uff/e04ufa at every iteration (see
Section 13). You can control the level of printed output (see the description of the optional parameter
Major Print Level). Note that the intermediate printout and final printout are produced only if
(the default for
e04uff, by default no output is produced by
e04ufa).
The following line of summary output (
characters) is produced at every major iteration. In all cases, the values of the quantities printed are those in effect
on completion of the given iteration.
Maj |
is the major iteration count.
|
Mnr |
is the number of minor iterations required by the feasibility and optimality phases of the QP subproblem. Generally, Mnr will be in the later iterations, since theoretical analysis predicts that the correct active set will be identified near the solution
(see Section 11).
Note that Mnr may be greater than the optional parameter Minor Iteration Limit if some iterations are required for the feasibility phase.
|
Step |
is the step taken along the computed search direction. On reasonably well-behaved problems, the unit step (i.e., ) will be taken as the solution is approached.
|
Merit Function |
is the value of the augmented Lagrangian merit function (12) at the current iterate. This function will decrease at each iteration unless it was necessary to increase the penalty parameters
(see Section 11.3).
As the solution is approached, Merit Function will converge to the value of the objective function at the solution.
If the QP subproblem does not have a feasible point (signified by I at the end of the current output line) then the merit function is a large multiple of the constraint violations, weighted by the penalty parameters. During a sequence of major iterations with infeasible subproblems, the sequence of Merit Function values will decrease monotonically until either a feasible subproblem is obtained or e04uff/e04ufa terminates with (no feasible point could be found for the nonlinear constraints).
If there are no nonlinear constraints present (i.e., ) then this entry contains Objective, the value of the objective function . The objective function will decrease monotonically to its optimal value when there are no nonlinear constraints.
|
Norm Gz |
is , the Euclidean norm of the projected gradient
(see Section 11.2).
Norm Gz will be approximately zero in the neighbourhood of a solution.
|
Violtn |
is the Euclidean norm of the residuals of constraints that are violated or in the predicted active set (not printed if ncnln is zero). Violtn will be approximately zero in the neighbourhood of a solution.
|
Cond Hz |
is a lower bound on the condition number of the projected Hessian approximation (; see (6)). The larger this number, the more difficult the problem.
|
M |
is printed if the quasi-Newton update has been modified to ensure that the Hessian approximation is positive definite
(see Section 11.4).
|
I |
is printed if the QP subproblem has no feasible point.
|
C |
is printed if central differences have been used to compute the unspecified objective and constraint gradients. If the value of Step is zero then the switch to central differences was made because no lower point could be found in the linesearch. (In this case, the QP subproblem is resolved with the central difference gradient and Jacobian.) If the value of Step is nonzero then central differences were computed because Norm Gz and Violtn imply that is close to a Kuhn–Tucker point (see Section 11.1).
|
L |
is printed if the linesearch has produced a relative change in greater than the value defined by the optional parameter Step Limit. If this output occurs frequently during later iterations of the run, optional parameter Step Limit should be set to a larger value.
|
R |
is printed if the approximate Hessian has been refactorized. If the diagonal condition estimator of indicates that the approximate Hessian is badly conditioned then the approximate Hessian is refactorized using column interchanges. If necessary, is modified so that its diagonal condition estimator is bounded.
|
The final printout includes a listing of the status of every variable and constraint.
The following describes the printout for each variable. A full stop (.) is printed for any numerical value that is zero.
Varbl |
gives the name (V) and index , for , of the variable.
|
State |
gives the state of the variable (FR if neither bound is in the working set, EQ if a fixed variable, LL if on its lower bound, UL if on its upper bound, TF if temporarily fixed at its current value). If Value lies outside the upper or lower bounds by more than the Feasibility Tolerance, State will be ++ or -- respectively.
A key is sometimes printed before State.
A |
Alternative optimum possible. The variable is active at one of its bounds, but its Lagrange multiplier is essentially zero. This means that if the variable were allowed to start moving away from its bound then there would be no change to 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 free, but it is equal to (or very close to) one of its bounds.
|
I |
Infeasible. The variable is currently violating one of its bounds by more than the Feasibility Tolerance.
|
|
Value |
is the value of the variable at the final iteration.
|
Lower Bound |
is the lower bound specified for the variable. None indicates that .
|
Upper Bound |
is the upper bound specified for the variable. None indicates that .
|
Lagr Mult |
is the Lagrange multiplier for the associated bound. This will be zero if State is FR unless and , in which case the entry will be blank. If is optimal, the multiplier should be non-negative if State is LL and non-positive if State is UL.
|
Slack |
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 linear and nonlinear constraints is the same as that given above for variables, with ‘variable’ replaced by ‘constraint’,
and
replaced by
and
respectively and with the following changes in the heading:
L Con |
gives the name (L) and index , for , of the linear constraint.
|
N Con |
gives the name (N) and index (), for , of the nonlinear 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 Slack column to become positive.
Numerical values are output with a fixed number of digits; they are not guaranteed to be accurate to this precision.
10
Example
This is based on Problem 71 in
Hock and Schittkowski (1981) and involves the minimization of the nonlinear function
subject to the bounds
to the general linear constraint
and to the nonlinear constraints
The initial point, which is infeasible, is
and
.
The optimal solution (to five figures) is
and
. One bound constraint and both nonlinear constraints are active at the solution.
10.1
Program Text
Note: the following programs illustrate the use of e04uff and e04ufa.
10.2
Program Data
10.3
Program Results
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 parameters which may be set by calls to
e04udf/e04uda and/or
e04uef/e04uea.
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 e04uff/e04ufa.
11.1
Overview
e04uff/e04ufa is essentially identical to the subroutine NPSOL described in
Gill et al. (1986b).
At a solution of
(1), some of the constraints will be
active, i.e., satisfied exactly. An active simple bound constraint implies that the corresponding variable is
fixed at its bound, and hence the variables are partitioned into
fixed and
free variables. Let
denote the
by
matrix of gradients of the active general linear and nonlinear constraints. The number of fixed variables will be denoted by
, with
(
) the number of free variables. The subscripts ‘FX’ and ‘FR’ on a vector or matrix will denote the vector or matrix composed of the elements corresponding to fixed or free variables.
A point
is a
first-order Kuhn–Tucker point for
(1) (see
Powell (1974)) if the following conditions hold:
-
(i) is feasible;
-
(ii)there exist vectors and (the Lagrange multiplier vectors for the bound and general constraints) such that
where is the gradient of evaluated at and if the th variable is free.
-
(iii)the Lagrange multiplier corresponding to an inequality constraint active at its lower bound must be non-negative. It is non-positive for an inequality constraint active at its upper bound.
Let
denote a matrix whose columns form a basis for the set of vectors orthogonal to the rows of
; i.e.,
. An equivalent statement of the condition
(2) in terms of
is
The vector
is termed the
projected gradient of
at
. Certain additional conditions must be satisfied in order for a first-order Kuhn–Tucker point to be a solution of
(1) (see
Powell (1974)).
e04uff/e04ufa implements a sequential quadratic programming (SQP) method. For an overview of SQP methods, see
Fletcher (1987),
Gill et al. (1981) and
Powell (1983).
The basic structure of
e04uff/e04ufa involves
major and
minor iterations. The major iterations generate a sequence of iterates
that converge to
, a first-order Kuhn–Tucker point of
(1). At a typical major iteration, the new iterate
is defined by
where
is the current iterate, the non-negative scalar
is the
step length, and
is the
search direction. (For simplicity, we shall always consider a typical iteration and avoid reference to the index of the iteration.) Also associated with each major iteration are estimates of the Lagrange multipliers and a prediction of the active set.
The search direction
in
(3) is the solution of a quadratic programming subproblem of the form
where
is the gradient of
at
, the matrix
is a positive definite quasi-Newton approximation to the Hessian of the Lagrangian function (see
Section 11.4), and
is the Jacobian matrix of
evaluated at
. (Finite difference estimates may be used for
and
; see the optional parameter
Derivative Level.) Let
in
(1) be partitioned into three sections:
,
and
, corresponding to the bound, linear and nonlinear constraints. The vector
in
(4) is similarly partitioned and is defined as
where
is the vector of nonlinear constraints evaluated at
. The vector
is defined in an analogous fashion.
The estimated Lagrange multipliers at each major iteration are the Lagrange multipliers from the subproblem
(4) (and similarly for the predicted active set). (The numbers of bounds, general linear and nonlinear constraints in the QP active set are the quantities
Bnd,
Lin and
Nln in the monitoring file output of
e04uff/e04ufa; see
Section 13.) In
e04uff/e04ufa,
(4) is solved using
e04ncf/e04nca. Since solving a quadratic program is itself an iterative procedure, the
minor iterations of
e04uff/e04ufa are the iterations of
e04ncf/e04nca. (More details about solving the subproblem are given in
Section 11.2.)
Certain matrices associated with the QP subproblem are relevant in the major iterations. Let the subscripts ‘FX’ and ‘FR’ refer to the
predicted fixed and free variables, and let
denote the
by
matrix of gradients of the general linear and nonlinear constraints in the predicted active set. Firstly, we have available the
factorization of
:
where
is a nonsingular
by
reverse-triangular matrix (i.e.,
if
), and the nonsingular
by
matrix
is the product of orthogonal transformations (see
Gill et al. (1984b)). Secondly, we have the upper triangular Cholesky factor
of the
transformed and reordered Hessian matrix
where
is the Hessian
with rows and columns permuted so that the free variables are first and
is the
by
matrix
with
the identity matrix of order
. If the columns of
are partitioned so that
then the
(
) columns of
form a basis for the null space of
. The matrix
is used to compute the projected gradient
at the current iterate. (The values
Nz and
Norm Gz printed by
e04uff/e04ufa give
and
, see
Section 13.)
A theoretical characteristic of SQP methods is that the predicted active set from the QP subproblem
(4) is identical to the correct active set in a neighbourhood of
. In
e04uff/e04ufa, this feature is exploited by using the QP active set from the previous iteration as a prediction of the active set for the next QP subproblem, which leads in practice to optimality of the subproblems in only one iteration as the solution is approached. Separate treatment of bound and linear constraints in
e04uff/e04ufa also saves computation in factorizing
and
.
Once
has been computed, the major iteration proceeds by determining a step length
that produces a ‘sufficient decrease’ in an augmented Lagrangian
merit function (see
Section 11.3). Finally, the approximation to the transformed Hessian matrix
is updated using a modified BFGS quasi-Newton update (see
Section 11.4) to incorporate new curvature information obtained in the move from
to
.
On entry to
e04uff/e04ufa, an iterative procedure from
e04ncf/e04nca is executed, starting with the user-supplied initial point, to find a point that is feasible with respect to the bounds and linear constraints (using the tolerance specified by the optional parameter
Linear Feasibility Tolerance). If no feasible point exists for the bound and linear constraints,
(1) has no solution and
e04uff/e04ufa terminates. Otherwise, the problem functions will thereafter be evaluated only at points that are feasible with respect to the bounds and linear constraints. The only exception involves variables whose bounds differ by an amount comparable to the finite difference interval (see the discussion of the optional parameter
Difference Interval). In contrast to the bounds and linear constraints, it must be emphasized that
the nonlinear constraints will not generally be satisfied until an optimal point is reached.
Facilities are provided to check whether the user-supplied gradients appear to be correct (see the description of the optional parameter
Verify). In general, the check is provided at the first point that is feasible with respect to the linear constraints and bounds. However, you may request that the check be performed at the initial point.
In summary, the method of
e04uff/e04ufa first determines a point that satisfies the bound and linear constraints. Thereafter, each iteration includes:
-
(a)the solution of a quadratic programming subproblem;
-
(b)a linesearch with an augmented Lagrangian merit function; and
-
(c)a quasi-Newton update of the approximate Hessian of the Lagrangian function.
These three procedures are described in more detail in
Sections 11.2 to
11.4.
11.2
Solution of the Quadratic Programming Subproblem
The search direction
is obtained by solving
(4) using
e04ncf/e04nca (see
Gill et al. (1986)), which was specifically designed to be used within an SQP algorithm for nonlinear programming.
e04ncf/e04nca is based on a two-phase (primal) quadratic programming method. The two phases of the method are: 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 subroutines. The two-phase nature of the algorithm is reflected by changing the function being minimized from the sum of infeasibilities to the quadratic objective function.
In general, a quadratic program must be solved by iteration. Let
denote the current estimate of the solution of
(4); the new iterate
is defined by
where, as in
(3),
is a non-negative step length and
is a search direction.
At the beginning of each iteration of
e04ncf/e04nca, a
working set is defined of constraints (general and bound) that are satisfied exactly. The vector
is then constructed so that the values of constraints in the working set remain
unaltered for any move along
. For a bound constraint in the working set, this property is achieved by setting the corresponding element of
to zero, i.e., by fixing the variable at its bound. As before, the subscripts ‘FX’ and ‘FR’ denote selection of the elements associated with the fixed and free variables.
Let
denote the sub-matrix of rows of
corresponding to general constraints in the working set. The general constraints in the working set will remain unaltered if
which is equivalent to defining
as
for some vector
, where
is the matrix associated with the
factorization
(5) of
.
The definition of
in
(10) depends on whether the current
is feasible. If not,
is zero except for an element
in the
th position, where
and
are chosen so that the sum of infeasibilities is decreasing along
. (For further details, see
Gill et al. (1986).) In the feasible case,
satisfies the equations
where
is the Cholesky factor of
and
is the gradient of the quadratic objective function
. (The vector
is the projected gradient of the QP.) With
(11),
is the minimizer of the quadratic objective function subject to treating the constraints in the working set as equalities.
If the QP projected gradient is zero, the current point is a constrained stationary point in the subspace defined by the working set. During the feasibility phase, the projected gradient will usually be zero only at a vertex (although it may vanish at non-vertices in the presence of constraint dependencies). During the optimality phase, a zero projected gradient implies that minimizes the quadratic objective function when the constraints in the working set are treated as equalities. In either case, Lagrange multipliers are computed. Given a positive constant of the order of the machine precision, the Lagrange multiplier corresponding to an inequality constraint in the working set is said to be optimal if when the th constraint is at its upper bound, or if when the associated constraint is at its lower bound. If any multiplier is nonoptimal, the current objective function (either the true objective or the sum of infeasibilities) can be reduced by deleting the corresponding constraint from the working set.
If optimal multipliers occur during the feasibility phase and the sum of infeasibilities is nonzero, no feasible point exists. The QP algorithm will then continue iterating to determine the minimum sum of infeasibilities. At this point, the Lagrange multiplier will satisfy for an inequality constraint at its upper bound, and for an inequality at its lower bound. The Lagrange multiplier for an equality constraint will satisfy .
The choice of step length
in the QP iteration
(8) is based on remaining feasible with respect to the satisfied constraints. During the optimality phase, if
is feasible,
will be taken as unity. (In this case, the projected gradient at
will be zero.) Otherwise,
is set to
, the step to the ‘nearest’ constraint, which is added to the working set at the next iteration.
Each change in the working set leads to a simple change to : if the status of a general constraint changes, a row of is altered; if a bound constraint enters or leaves the working set, a column of changes. Explicit representations are recurred of the matrices , and , and of the vectors and .
11.3
The Merit Function
After computing the search direction as described in
Section 11.2, each major iteration proceeds by determining a step length
in
(3) that produces a ‘sufficient decrease’ in the augmented Lagrangian merit function
where
,
and
vary during the linesearch. The summation terms in
(12) involve only the
nonlinear constraints. The vector
is an estimate of the Lagrange multipliers for the nonlinear constraints of
(1). The non-negative
slack variables
allow nonlinear inequality constraints to be treated without introducing discontinuities. The solution of the QP subproblem
(4) provides a vector triple that serves as a direction of search for the three sets of variables. The non-negative vector
of
penalty parameters is initialized to zero at the beginning of the first major iteration. Thereafter, selected elements are increased whenever necessary to ensure descent for the merit function. Thus, the sequence of norms of
(the printed quantity
Penalty; see
Section 13) is generally nondecreasing, although each
may be reduced a limited number of times.
The merit function
(12) and its global convergence properties are described in
Gill et al. (1986a).
11.4
The Quasi-Newton Update
The matrix
in
(4) is a
positive definite quasi-Newton approximation to the Hessian of the Lagrangian function. (For a review of quasi-Newton methods, see
Dennis and Schnabel (1983).) At the end of each major iteration, a new Hessian approximation
is defined as a rank-two modification of
. In
e04uff/e04ufa, the BFGS (Broyden–Fletcher–Goldfarb–Shanno) quasi-Newton update is used:
where
(the change in
).
In
e04uff/e04ufa,
is required to be positive definite. If
is positive definite,
defined by
(13) will be positive definite if and only if
is positive (see
Dennis and Moré (1977)). Ideally,
in
(13) would be taken as
, the change in gradient of the Lagrangian function
where
denotes the QP multipliers associated with the nonlinear constraints of the original problem. If
is not sufficiently positive, an attempt is made to perform the update with a vector
of the form
where
. If no such vector can be found, the update is performed with a scaled
. In this case,
M is printed to indicate that the update was modified.
Rather than modifying
itself, the Cholesky factor of the
transformed Hessian
(6) is updated, where
is the matrix from
(5) associated with the active set of the QP subproblem. The update
(13) is equivalent to the following update to
:
where
, and
. This update may be expressed as a
rank-one update to
(see
Dennis and Schnabel (1981)).
12
Optional Parameters
Several optional parameters in e04uff/e04ufa define choices in the problem specification or the algorithm logic. In order to reduce the complexity of using e04uff/e04ufa these optional parameters have associated default values that are appropriate for most problems. Therefore you need only specify those optional parameters 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 parameters.
The following is a list of the optional parameters available. A full description of each optional parameter is provided in
Section 12.1.
Optional parameters may be specified by calling one, or both, of
e04udf/e04uda and
e04uef/e04uea before a call to
e04uff/e04ufa.
e04udf/e04uda 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 parameter. For example,
Begin * Example options file
Print level = 5
End
The call
Call e04udf (ioptns, inform)
can then be used to read the file on unit
ioptns.
on successful exit.
e04udf/e04uda should be consulted for a full description of this method of supplying optional parameters.
e04uef/e04uea can be called to supply options directly, one call being necessary for each optional parameter. For example,
Call e04uef ('Print Level = 1')
e04uef/e04uea should be consulted for a full description of this method of supplying optional parameters.
All optional parameters not specified are set to their default values. Optional parameters specified are unaltered by e04uff/e04ufa (unless they define invalid values) and so remain in effect for subsequent calls to e04uff/e04ufa.
12.1
Description of the Optional Parameters
For each option, we give a summary line, a description of the optional parameter 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 , and denote options that take character, integer and real values respectively;
- the default value, where the symbol is a generic notation for machine precision (see x02ajf), and denotes the relative precision of the objective function Function Precision.
Keywords and character values are case and white space insensitive.
Central Difference Interval | | Default values are computed |
If the algorithm switches to central differences because the forward-difference approximation is not sufficiently accurate, the value of
is used as the difference interval for every element of
. The switch to central differences is indicated by
C at the end of each line of intermediate printout produced by the major iterations (see
Section 9.1). The use of finite differences is discussed further under the optional parameter
Difference Interval.
If you supply a value for this optional parameter, a small value between and is appropriate.
This option controls the specification of the initial working set in both the procedure for finding a feasible point for the linear constraints and bounds and in the first QP subproblem thereafter. With a
Cold Start, the first working set is chosen by
e04uff/e04ufa based on the values of the variables and constraints at the initial point. Broadly speaking, the initial working set will include equality constraints and bounds or inequality constraints that violate or ‘nearly’ satisfy their bounds (to within
Crash Tolerance).
With a
Warm Start, you must set the
istate array and define
clamda and
r as discussed in
Section 5.
istate values associated with bounds and linear constraints determine the initial working set of the procedure to find a feasible point with respect to the bounds and linear constraints.
istate values associated with nonlinear constraints determine the initial working set of the first QP subproblem after such a feasible point has been found.
e04uff/e04ufa will override your specification of
istate if necessary, so that a poor choice of the working set will not cause a fatal error. For instance, any elements of
istate which are set to
,
will be reset to zero, as will any elements which are set to
when the corresponding elements of
bl and
bu are not equal. A warm start will be advantageous if a good estimate of the initial working set is available – for example, when
e04uff/e04ufa is called repeatedly to solve related problems.
Crash Tolerance | | Default |
This value is used in conjunction with the optional parameter
Cold Start (the default value) when
e04uff/e04ufa selects an initial working set. If
, the initial working set will include (if possible) bounds or general inequality constraints that lie within
of their bounds. In particular, a constraint of the form
will be included in the initial working set if
. If
or
, the default value is used.
This special keyword may be used to reset all optional parameters to their default values.
Derivative Level | | Default |
This parameter indicates which derivatives are provided during intermediate exits. The possible choices for
are the following.
|
Meaning |
3 |
All elements of the objective gradient and the constraint Jacobian are provided. |
2 |
All elements of the constraint Jacobian are provided, but some elements of the objective gradient are not specified. |
1 |
All elements of the objective gradient are provided, but some elements of the constraint Jacobian are not specified. |
0 |
Some elements of both the objective gradient and the constraint Jacobian are not specified. |
The value should be used whenever possible, since e04uff/e04ufa is more reliable (and will usually be more efficient) when all derivatives are exact.
If
,
e04uff/e04ufa will estimate the unspecified elements of the objective gradient, using finite differences. The computation of finite difference approximations usually increases the total run-time, since an intermediate exit to the calling program is required for each unspecified element. Furthermore, less accuracy can be attained in the solution (see Chapter 8 of
Gill et al. (1981), for a discussion of limiting accuracy).
If
,
e04uff/e04ufa will approximate unspecified elements of the constraint Jacobian. One intermediate exit is needed for each variable for which partial derivatives are not available. For example, if the Jacobian has the form
where ‘
’ indicates an element provided and ‘?’ indicates an unspecified element,
e04uff/e04ufa will make an intermediate exit to the calling program twice: once to estimate the missing element in column
, and again to estimate the two missing elements in column
. (Since columns
and
are known, they require no intermediate exits for information.)
At times, central differences are used rather than forward differences, in which case twice as many intermediate exits are needed. (The switch to central differences is not under your control.)
If or , the default value is used.
Difference Interval | | Default values are computed |
This option defines an interval used to estimate derivatives by finite differences in the following circumstances:
-
(a)For verifying the objective and/or constraint gradients (see the description of the optional parameter Verify).
-
(b)For estimating unspecified elements of the objective gradient or the constraint Jacobian.
In general, a derivative with respect to the
th variable is approximated using the interval
, where
, with
the first point feasible with respect to the bounds and linear constraints. If the functions are well scaled, then the resulting derivative approximation should be accurate to
. See
Gill et al. (1981) for a discussion of the accuracy in finite difference approximations.
If a difference interval is not specified, then a finite difference interval will be computed automatically for each variable by a procedure that requires up to six intermediate exits for each element. This option is recommended if the function is badly scaled or you wish to have e04uff/e04ufa determine constant elements in the objective and constraint gradients.
If you supply a value for this optional parameter, a small value between and is appropriate.
Feasibility Tolerance | | Default |
The scalar
defines the maximum acceptable
absolute violations in linear and nonlinear constraints at a ‘feasible’ point; i.e., a constraint is considered satisfied if its violation does not exceed
. If
or
, the default value is used. Using this keyword sets both optional parameters
Linear Feasibility Tolerance and
Nonlinear Feasibility Tolerance to
, if
. (Additional details are given under the descriptions of these optional parameters.)
Function Precision | | Default |
This parameter defines , which is intended to be a measure of the accuracy with which the problem functions and can be computed. If or , the default value is used.
The value of
should reflect the relative precision of
; i.e.,
acts as a relative precision when
is large and as an absolute precision when
is small. For example, if
is typically of order
and the first six significant digits are known to be correct, an appropriate value for
would be
. In contrast, if
is typically of order
and the first six significant digits are known to be correct, an appropriate value for
would be
. The choice of
can be quite complicated for badly scaled problems; see Chapter 8 of
Gill et al. (1981) for a discussion of scaling techniques. The default value is appropriate for most simple functions that are computed with full accuracy. However, when the accuracy of the computed function values is known to be significantly worse than full precision, the value of
should be large enough so that
e04uff/e04ufa will not attempt to distinguish between function values that differ by less than the error inherent in the calculation.
This option controls the contents of the upper triangular matrix
(see
Section 5).
e04uff/e04ufa works exclusively with the
transformed and reordered Hessian
(6), and hence extra computation is required to form the Hessian itself. If
,
r contains the Cholesky factor of the transformed and reordered Hessian. If
, the Cholesky factor of the approximate Hessian itself is formed and stored in
r. You should select
if a
Warm Start will be used for the next call to
e04uff/e04ufa.
Infinite Bound Size | | Default |
If , 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 ). If , the default value is used.
Infinite Step Size | | Default |
If , specifies the magnitude of the change in variables that is treated as a step to an unbounded solution. 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. If , the default value is used.
Line Search Tolerance | | Default |
The value () controls the accuracy with which the step taken during each iteration approximates a minimum of the merit function along the search direction (the smaller the value of , the more accurate the linesearch). The default value requests an inaccurate search and is appropriate for most problems, particularly those with any nonlinear constraints.
If there are no nonlinear constraints, a more accurate search may be appropriate when it is desirable to reduce the number of major iterations – for example, if the objective function is cheap to evaluate, or if a substantial number of derivatives are unspecified. If or , the default value is used.
Linear Feasibility Tolerance | | Default |
Nonlinear Feasibility Tolerance | | Default or |
The default value of is if or , and otherwise.
The scalars and define the maximum acceptable absolute violations in linear and nonlinear constraints at a ‘feasible’ point; i.e., a linear constraint is considered satisfied if its violation does not exceed . Similarly a nonlinear constraint is considered satisfied if its violation does not exceed . If or , the default value is used, for .
On entry to e04uff/e04ufa, an iterative procedure is executed in order to find a point that satisfies the linear constraints and bounds on the variables to within the tolerance . All subsequent iterates will satisfy the linear constraints to within the same tolerance (unless is comparable to the finite difference interval).
For nonlinear constraints, the feasibility tolerance
defines the largest constraint violation that is acceptable at an optimal point. Since nonlinear constraints are generally not satisfied until the final iterate, the value of optional parameter
Nonlinear Feasibility Tolerance acts as a partial termination criterion for the iterative sequence generated by
e04uff/e04ufa (see the discussion of optional parameter
Optimality Tolerance).
These tolerances should reflect the precision of the corresponding constraints. For example, if the variables and the coefficients in the linear constraints are of order unity, and the latter are correct to about decimal digits, it would be appropriate to specify as .
Nolist | | Default for e04ufa |
Optional parameter
List enables printing of each optional parameter specification as it is supplied.
Nolist suppresses this printing.
Major Iteration Limit | | Default |
The value of specifies the maximum number of major iterations allowed before termination. Setting and means that the workspace needed will be computed and printed, but no iterations will be performed. If , the default value is used.
Major Print Level | | Default for e04uff
|
Print Level | | Default for e04ufa
|
The value of
controls the amount of printout produced by the major iterations of
e04uff/e04ufa, as indicated below. A detailed description of the printed output is given in
Section 9.1 (summary output at each major iteration and the final solution) and
Section 13 (monitoring information at each major iteration). (See also the description of the optional parameter
Minor Print Level.)
The following printout is sent to the current advisory message unit (as defined by
x04abf):
|
Output |
|
No output. |
|
The final solution only. |
|
One line of summary output ( characters; see Section 9.1) for each major iteration (no printout of the final solution). |
|
The final solution and one line of summary output for each major iteration. |
The following printout is sent to the unit number given by the optional parameter
Monitoring File:
|
Output |
|
No output. |
|
One long line of output ( characters; see Section 13) for each major iteration (no printout of the final solution). |
|
At each major iteration, the objective function, the Euclidean norm of the nonlinear constraint violations, the values of the nonlinear constraints (the vector ), the values of the linear constraints (the vector ), and the current values of the variables (the vector ). |
|
At each major iteration, the diagonal elements of the matrix associated with the factorization (5) (see Section 11.1) of the QP working set, and the diagonal elements of , the triangular factor of the transformed and reordered Hessian (6) (see Section 11.1). |
If
and the unit number defined by the optional parameter
Monitoring File is the same as that defined by
x04abf, the summary output for each major iteration is suppressed.
Minor Iteration Limit | | Default |
The value of specifies the maximum number of iterations for finding a feasible point with respect to the bounds and linear constraints (if any). The value of also specifies the maximum number of minor iterations for the optimality phase of each QP subproblem. If , the default value is used.
Minor Print Level | | Default |
The value of
controls the amount of printout produced by the minor iterations of
e04uff/e04ufa (i.e., the iterations of the quadratic programming algorithm), as indicated below. A detailed description of the printed output is given in
Section 9.2 in
e04ncf/e04nca (summary output at each minor iteration and the final QP solution) and
Section 13 in
e04ncf/e04nca) (monitoring information at each minor iteration).
(See also the description of the optional parameter
Major Print Level.)
The following printout is sent to the current advisory message unit (as defined by
x04abf):
|
Output |
|
No output. |
|
The final QP solution only. |
|
One line of summary output ( characters; see Section 9.2 in e04ncf/e04nca)
for each minor iteration (no printout of the final QP solution). |
|
The final QP solution and one line of summary output for each minor iteration. |
The following printout is sent to the unit number given by the optional parameter
Monitoring File:
|
Output |
|
No output. |
|
One long line of output ( characters; see
Section 9.2 in e04ncf/e04nca)
for each minor iteration (no printout of the final QP solution). |
|
At each minor iteration, the current estimates of the QP multipliers, the current estimate of the QP search direction, the QP constraint values and the status of each QP constraint. |
|
At each minor iteration, the diagonal elements of the matrix associated with the factorization (5) (see Section 11.1) of the QP working set and the diagonal elements of the Cholesky factor of the transformed Hessian (6) (see Section 11.1). |
If
and the unit number defined by the optional parameter
Monitoring File is the same as that defined by
x04abf, the summary output for each major iteration is suppressed.
Monitoring File | | Default |
If
and
or
and
, monitoring information produced by e04uff/e04ufa at every iteration is sent to
a file with logical unit number .
If
and/or
and , no monitoring information is produced.
Optimality Tolerance | | Default |
The parameter () specifies the accuracy to which you wish the final iterate to approximate a solution of the problem. Broadly speaking, indicates the number of correct figures desired in the objective function at the solution. For example, if is and e04uff/e04ufa terminates successfully, the final value of should have approximately six correct figures. If or , the default value is used.
e04uff/e04ufa will terminate successfully if the iterative sequence of
values is judged to have converged and the final point satisfies the first-order Kuhn–Tucker conditions (see
Section 11.1). The sequence of iterates is considered to have converged at
if
where
is the search direction and
the step length from
(3). An iterate is considered to satisfy the first-order conditions for a minimum if
and
where
is the projected gradient (see
Section 11.1),
is the gradient of
with respect to the free variables,
is the violation of the
th active nonlinear constraint, and
is the
Nonlinear Feasibility Tolerance.
Start Objective Check At Variable | | Default |
Stop Objective Check At Variable | | Default |
Start Constraint Check At Variable | | Default |
Stop Constraint Check At Variable | | Default |
These keywords take effect only if . They may be used to control the verification of gradient elements and/or Jacobian elements computed by the calling program during intermediate exits. For example, if the first elements of the objective gradient appeared to be correct in an earlier run, so that only element remains questionable, it is reasonable to specify . If the first variables appear linearly in the objective, so that the corresponding gradient elements are constant, the above choice would also be appropriate.
If or , the default value is used, for . If or , the default value is used, for .
If
specifies the maximum change in variables at the first step of the linesearch. In some cases, such as
or
, even a moderate change in the elements of
can lead to floating-point overflow. The parameter
is therefore used to encourage evaluation of the problem functions at meaningful points. Given any major iterate
, the first point
at which
and
are evaluated during the linesearch is restricted so that
The linesearch may go on and evaluate
and
at points further from
if this will result in a lower value of the merit function (indicated by
L at the end of each line of output produced by the major iterations; see
Section 9.1). If
L is printed for most of the iterations,
should be set to a larger value.
Wherever possible, upper and lower bounds on
should be used to prevent evaluation of nonlinear functions at wild values. The default value
should not affect progress on well-behaved functions, but values such as
may be helpful when rapidly varying functions are present. If a small value of
Step Limit is selected, then a good starting point may be required. An important application is to the class of nonlinear least squares problems. If
, the default value is used.
Verify Constraint Gradients | | |
Verify Objective Gradients | | |
These keywords refer to finite difference checks on the gradient elements computed by the calling program during intermediate exits. (Unspecified gradient elements are not checked.) The possible choices for
are as follows:
|
Meaning |
|
No checks are performed. |
|
Only a ‘cheap’ test will be performed. |
|
In addition to the ‘cheap’ test, individual gradient elements will also be checked using a reliable (but more expensive) test. |
It is possible to specify
to
in several ways. For example, the objective gradient will be verified if
Verify,
,
Verify Gradients,
Verify Objective Gradients or
is specified. The constraint gradients will be verified if
or
or
Verify is specified. Similarly, the objective and the constraint gradients will be verified if
or
or
Verify is specified.
If , gradients will be verified at the first point that satisfies the linear constraints and bounds.
If , only a ‘cheap’ test will be performed, requiring one intermediate exit for the objective function gradients and (if appropriate) one intermediate exit for the partial derivatives of the constraints.
If
, a more reliable (but more expensive) check will be made on individual gradient elements, within the ranges specified by the
Start Objective Check At Variable and
Stop Objective Check At Variable keywords. A result of the form
OK or
BAD? is printed by
e04uff/e04ufa to indicate whether or not each element appears to be correct. If a gradient element is determined to be extremely poor (i.e., if it appears to have no significant digits of accuracy at all), then
e04uff/e04ufa will also exit with an error indicator in argument
ifail.
If , the action is the same as for , except that it will take place at the user-specified initial value of .
If or or , the default value is used.
We suggest that be used whenever a new calling program is being developed.
13
Description of Monitoring Information
This section describes the long line of output (
characters) which forms part of the monitoring information produced by
e04uff/e04ufa. (See also the description of the optional parameters
Major Print Level,
Minor Print Level and
Monitoring File.) You can control the level of printed output (see the description of the optional parameter
Major Print Level).
When
and
, the following line of output is produced at every major iteration of
e04uff/e04ufa
on the unit number
specified by
Monitoring File. In all cases, the values of the quantities printed are those in effect
on completion of the given iteration.
Maj |
is the major iteration count.
|
Mnr |
is the number of minor iterations required by the feasibility and optimality phases of the QP subproblem. Generally, Mnr will be in the later iterations, since theoretical analysis predicts that the correct active set will be identified near the solution
(see Section 11).
Note that Mnr may be greater than the optional parameter Minor Iteration Limit if some iterations are required for the feasibility phase.
|
Step |
is the step taken along the computed search direction. On reasonably well-behaved problems, the unit step (i.e., ) will be taken as the solution is approached.
|
Nfun |
is the cumulative number of evaluations of the objective function needed for the linesearch. Evaluations needed for the estimation of the gradients by finite differences are not included. Nfun is printed as a guide to the amount of work required for the linesearch.
|
Merit Function |
is the value of the augmented Lagrangian merit function (12) at the current iterate. This function will decrease at each iteration unless it was necessary to increase the penalty parameters
(see Section 11.3).
As the solution is approached, Merit Function will converge to the value of the objective function at the solution.
If the QP subproblem does not have a feasible point (signified by I at the end of the current output line) then the merit function is a large multiple of the constraint violations, weighted by the penalty parameters. During a sequence of major iterations with infeasible subproblems, the sequence of Merit Function values will decrease monotonically until either a feasible subproblem is obtained or e04uff/e04ufa terminates with (no feasible point could be found for the nonlinear constraints).
If there are no nonlinear constraints present (i.e., ) then this entry contains Objective, the value of the objective function . The objective function will decrease monotonically to its optimal value when there are no nonlinear constraints.
|
Norm Gz |
is , the Euclidean norm of the projected gradient
(see Section 11.2).
Norm Gz will be approximately zero in the neighbourhood of a solution.
|
Violtn |
is the Euclidean norm of the residuals of constraints that are violated or in the predicted active set (not printed if ncnln is zero). Violtn will be approximately zero in the neighbourhood of a solution.
|
Nz |
is the number of columns of (see Section 11.2). The value of Nz is the number of variables minus the number of constraints in the predicted active set; i.e., .
|
Bnd |
is the number of simple bound constraints in the predicted active set.
|
Lin |
is the number of general linear constraints in the predicted working set.
|
Nln |
is the number of nonlinear constraints in the predicted active set (not printed if ncnln is zero).
|
Penalty |
is the Euclidean norm of the vector of penalty parameters used in the augmented Lagrangian merit function (not printed if ncnln is zero).
|
Cond H |
is a lower bound on the condition number of the Hessian approximation .
|
Cond Hz |
is a lower bound on the condition number of the projected Hessian approximation (; see (6)). The larger this number, the more difficult the problem.
|
Cond T |
is a lower bound on the condition number of the matrix of predicted active constraints.
|
Conv |
is a three-letter indication of the status of the three convergence tests (16)–(18) defined in the description of the optional parameter Optimality Tolerance. Each letter is T if the test is satisfied and F otherwise. The three tests indicate whether:
-
(i)the sequence of iterates has converged;
-
(ii)the projected gradient (Norm Gz) is sufficiently small; and
-
(iii)the norm of the residuals of constraints in the predicted active set (Violtn) is small enough.
If any of these indicators is F when e04uff/e04ufa terminates with , you should check the solution carefully.
|
M |
is printed if the quasi-Newton update has been modified to ensure that the Hessian approximation is positive definite
(see Section 11.4).
|
I |
is printed if the QP subproblem has no feasible point.
|
C |
is printed if central differences have been used to compute the unspecified objective and constraint gradients. If the value of Step is zero then the switch to central differences was made because no lower point could be found in the linesearch. (In this case, the QP subproblem is resolved with the central difference gradient and Jacobian.) If the value of Step is nonzero then central differences were computed because Norm Gz and Violtn imply that is close to a Kuhn–Tucker point (see Section 11.1).
|
L |
is printed if the linesearch has produced a relative change in greater than the value defined by the optional parameter Step Limit. If this output occurs frequently during later iterations of the run, optional parameter Step Limit should be set to a larger value.
|
|
On entry: need not be initialized if the (default) optional parameter Cold Start is used.
If the optional parameter Warm Start has been chosen, r must contain the upper triangular Cholesky factor of the initial approximation of the Hessian of the Lagrangian function, with the variables in the natural order. Elements not in the upper triangular part of r are assumed to be zero and need not be assigned.
On exit: if , r contains the upper triangular Cholesky factor of , an estimate of the transformed and reordered Hessian of the Lagrangian at (see (6) in Section 11.1). If , r contains the upper triangular Cholesky factor of , the approximate (untransformed) Hessian of the Lagrangian, with the variables in the natural order.
|