NAG FL Interface
e05ucf (nlp_​multistart_​sqp)

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

1: $\mathbf{n}$ – Integer Input

On entry: $n$, the number of variables.

Constraint: $\mathbf{n}>0$.

2: $\mathbf{nclin}$ – Integer Input

On entry: $n_L$, the number of general linear constraints.

Constraint: $\mathbf{nclin}\ge 0$.

3: $\mathbf{ncnln}$ – Integer Input

On entry: $n_N$, the number of nonlinear constraints.

Constraint: $\mathbf{ncnln}\ge 0$.

4: $\mathbf{a}(\mathbf{lda},*)$ – Real (Kind=nag_wp) array Input

Note: the second dimension of the array a must be at least $\mathbf{n}$ if $\mathbf{nclin}>0$, and at least $1$ otherwise.

On entry: the matrix $A_L$ of general linear constraints in (1). That is, the $\mathit{i}$th row contains the coefficients of the $\mathit{i}$th general linear constraint, for $\mathit{i}=1,2,\dots ,\mathbf{nclin}$.

If $\mathbf{nclin}=0$, the array a is not referenced.

5: $\mathbf{lda}$ – Integer Input

On entry: the first dimension of the array a as declared in the (sub)program from which e05ucf is called.

Constraint: $\mathbf{lda}\ge \mathbf{nclin}$.

6: $\mathbf{bl}\left(\mathbf{n}+\mathbf{nclin}+\mathbf{ncnln}\right)$ – Real (Kind=nag_wp) array Input

7: $\mathbf{bu}\left(\mathbf{n}+\mathbf{nclin}+\mathbf{ncnln}\right)$ – Real (Kind=nag_wp) array Input

On entry: bl must contain the lower bounds and bu the upper bounds for all the constraints in the following order. The first $n$ elements of each array must contain the bounds on the variables, the next $n_L$ elements the bounds for the general linear constraints (if any) and the next $n_N$ elements the bounds for the general nonlinear constraints (if any). To specify a nonexistent lower bound (i.e., $l_j=-\infty$), set $\mathbf{bl}\left(j\right)\le -\mathit{bigbnd}$, and to specify a nonexistent upper bound (i.e., $u_j=+\infty$), set $\mathbf{bu}\left(j\right)\ge \mathit{bigbnd}$; the default value of $\mathit{bigbnd}$ is ${10}^{20}$, but this may be changed by the optional parameter Infinite Bound Size. To specify the $j$th constraint as an equality, set $\mathbf{bl}\left(j\right)=\mathbf{bu}\left(j\right)=\beta$, say, where $\left|\beta \right|<\mathit{bigbnd}$.

Constraints:

• $\mathbf{bl}\left(\mathit{j}\right)\le \mathbf{bu}\left(\mathit{j}\right)$, for $\mathit{j}=1,2,\dots ,\mathbf{n}+\mathbf{nclin}+\mathbf{ncnln}$;
• if $\mathbf{bl}\left(j\right)=\mathbf{bu}\left(j\right)=\beta$, $\left|\beta \right|<\mathit{bigbnd}$.

8: $\mathbf{confun}$ – Subroutine, supplied by the NAG Library or the user. External Procedure

confun must calculate the vector $c\left(x\right)$ of nonlinear constraint functions and (optionally) its Jacobian ($=\frac{\partial c}{\partial x}$) for a specified $n$-element vector $x$. If there are no nonlinear constraints (i.e., $\mathbf{ncnln}=0$), confun will never be called by e05ucf and confun may be the dummy routine e04udm. (e04udm is included in the NAG Library.) If there are nonlinear constraints, the first call to confun will occur before the first call to objfun.

The specification of confun is:

Fortran Interface copy

Subroutine confun ( mode, ncnln, n, ldcjsl, needc, x, c, cjsl, nstate, iuser, ruser) Integer, Intent (In) :: ncnln, n, ldcjsl, needc(ncnln), nstate Integer, Intent (Inout) :: mode, iuser(*) Real (Kind=nag_wp), Intent (In) :: x(n) Real (Kind=nag_wp), Intent (Inout) :: cjsl(ldcjsl,n), ruser(*) Real (Kind=nag_wp), Intent (Out) :: c(ncnln)

C Header Interface copy

void  confun (Integer *mode, const Integer *ncnln, const Integer *n, const Integer *ldcjsl, const Integer needc[], const double x[], double c[], double cjsl[], const Integer *nstate, Integer iuser[], double ruser[])

C++ Header Interface copy

#include <nag.h> extern "C" { void  confun (Integer &mode, const Integer &ncnln, const Integer &n, const Integer &ldcjsl, const Integer needc[], const double x[], double c[], double cjsl[], const Integer &nstate, Integer iuser[], double ruser[]) }

1: $\mathbf{mode}$ – Integer Input/Output

On entry: indicates which values must be assigned during each call of confun. Only the following values need be assigned, for each value of $i$ such that $\mathbf{needc}\left(i\right)>0$:

$\mathbf{mode}=0$

$\mathbf{c}\left(i\right)$.

$\mathbf{mode}=1$

All available elements in the $i$th row of cjsl.

$\mathbf{mode}=2$

$\mathbf{c}\left(i\right)$ and all available elements in the $i$th row of cjsl.

On exit: may be set to a negative value if you wish to abandon the solution to the current local minimization problem. In this case e05ucf will move to the next local minimization problem.

2: $\mathbf{ncnln}$ – Integer Input

On entry: $n_N$, the number of nonlinear constraints.

3: $\mathbf{n}$ – Integer Input

On entry: $n$, the number of variables.

4: $\mathbf{ldcjsl}$ – Integer Input

On entry: ldcjsl is the same value as ldcjac in the call to e05ucf.

5: $\mathbf{needc}\left(\mathbf{ncnln}\right)$ – Integer array Input

On entry: the indices of the elements of c and/or cjsl that must be evaluated by confun. If $\mathbf{needc}\left(i\right)>0$, $\mathbf{c}\left(i\right)$ and/or the available elements of the $i$th row of cjsl (see argument mode) must be evaluated at $x$.

6: $\mathbf{x}\left(\mathbf{n}\right)$ – Real (Kind=nag_wp) array Input

On entry: $x$, the vector of variables at which the constraint functions and/or the available elements of the constraint Jacobian are to be evaluated.

7: $\mathbf{c}\left(\mathbf{ncnln}\right)$ – Real (Kind=nag_wp) array Output

On exit: if $\mathbf{needc}\left(k\right)>0$ and $\mathbf{mode}=0$ or $2$, $\mathbf{c}\left(k\right)$ must contain the value of $c_k\left(x\right)$. The remaining elements of c, corresponding to the non-positive elements of needc, need not be set.

8: $\mathbf{cjsl}(\mathbf{ldcjsl},\mathbf{n})$ – Real (Kind=nag_wp) array Input/Output

cjsl may be regarded as a two-dimensional ‘slice’ of the three-dimensional array cjac of e05ucf.

On entry: unless $\mathbf{Derivative\; Level}=2$ or $3$ (the default setting is $\mathbf{Derivative\; Level}=3$), the elements of cjsl are set to special values which enable e05ucf to detect whether they are changed by confun.

On exit: if $\mathbf{needc}\left(k\right)>0$ and $\mathbf{mode}=1$ or $2$, the $k$th row of cjsl must contain the available elements of the vector $\nabla c_k$ given by

$$\nabla c_k={(\frac{\partial c_k}{\partial x_1},\frac{\partial c_k}{\partial x_2},\dots ,\frac{\partial c_k}{\partial x_n})}^{\mathrm{T}}\text{,}$$

where $\frac{\partial c_k}{\partial x_j}$ is the partial derivative of the $k$th constraint with respect to the $j$th variable, evaluated at the point $x$. See also the argument nstate. The remaining rows of cjsl, corresponding to non-positive elements of needc, need not be set.

If all elements of the constraint Jacobian are known (i.e., $\mathbf{Derivative\; Level}=2$ or $3$), any constant elements may be assigned to cjsl one time only at the start of each local optimization. An element of cjsl that is not subsequently assigned in confun will retain its initial value throughout the local optimization. Constant elements may be loaded into cjsl during the first call to confun for the local optimization (signalled by the value $\mathbf{nstate}=1$). The ability to preload constants is useful when many Jacobian elements are identically zero, in which case cjsl may be initialized to zero and nonzero elements may be reset by confun.

Note that constant nonzero elements do affect the values of the constraints. Thus, if $\mathbf{cjsl}(k,j)$ is set to a constant value, it need not be reset in subsequent calls to confun, but the value $\mathbf{cjsl}(k,j)\times \mathbf{x}\left(j\right)$ must nonetheless be added to $\mathbf{c}\left(k\right)$. For example, if $\mathbf{cjsl}(1,1)=2$ and $\mathbf{cjsl}(1,2)=\mathrm{-5}$ then the term $2\times \mathbf{x}\left(1\right)-5\times \mathbf{x}\left(2\right)$ must be included in the definition of $\mathbf{c}\left(1\right)$.

It must be emphasized that, if $\mathbf{Derivative\; Level}=0$ or $1$, unassigned elements of cjsl 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 $x$ is computed automatically at the start of each local optimization. The automatic procedure can usually identify constant elements of cjsl, which are then computed once only by finite differences.

9: $\mathbf{nstate}$ – Integer Input

On entry: if $\mathbf{nstate}=1$ then e05ucf is calling confun for the first time on the current local optimization problem. This argument setting allows you to save computation time if certain data must be calculated only once.

10: $\mathbf{iuser}\left(*\right)$ – Integer array User Workspace

11: $\mathbf{ruser}\left(*\right)$ – Real (Kind=nag_wp) array User Workspace

confun is called with the arguments iuser and ruser as supplied to e05ucf. You should use the arrays iuser and ruser to supply information to confun.

confun must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which e05ucf is called. Arguments denoted as Input must not be changed by this procedure.

Note: confun should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by e05ucf. If your code inadvertently does return any NaNs or infinities, e05ucf is likely to produce unexpected results.

confun should be tested separately before being used in conjunction with e05ucf. See also the description of the optional parameter Verify.

9: $\mathbf{objfun}$ – Subroutine, supplied by the user. External Procedure

objfun must calculate the objective function $F\left(x\right)$ and (optionally) its gradient $g\left(x\right)=\frac{\partial F}{\partial x}$ for a specified $n$-vector $x$.

The specification of objfun is:

Fortran Interface copy

Subroutine objfun ( mode, n, x, objf, objgrd, nstate, iuser, ruser) Integer, Intent (In) :: n, nstate Integer, Intent (Inout) :: mode, iuser(*) Real (Kind=nag_wp), Intent (In) :: x(n) Real (Kind=nag_wp), Intent (Inout) :: objgrd(n), ruser(*) Real (Kind=nag_wp), Intent (Out) :: objf

C Header Interface copy

void  objfun (Integer *mode, const Integer *n, const double x[], double *objf, double objgrd[], const Integer *nstate, Integer iuser[], double ruser[])

C++ Header Interface copy

#include <nag.h> extern "C" { void  objfun (Integer &mode, const Integer &n, const double x[], double &objf, double objgrd[], const Integer &nstate, Integer iuser[], double ruser[]) }

1: $\mathbf{mode}$ – Integer Input/Output

On entry: indicates which values must be assigned during each call of objfun. Only the following values need be assigned:

$\mathbf{mode}=0$

objf.

$\mathbf{mode}=1$

All available elements of objgrd.

$\mathbf{mode}=2$

objf and all available elements of objgrd.

On exit: may be set to a negative value if you wish to abandon the solution to the current local minimization problem. In this case e05ucf will move to the next local minimization problem.

2: $\mathbf{n}$ – Integer Input

On entry: $n$, the number of variables.

3: $\mathbf{x}\left(\mathbf{n}\right)$ – Real (Kind=nag_wp) array Input

On entry: $x$, the vector of variables at which the objective function and/or all available elements of its gradient are to be evaluated.

4: $\mathbf{objf}$ – Real (Kind=nag_wp) Output

On exit: if $\mathbf{mode}=0$ or $2$, objf must be set to the value of the objective function at $x$.

5: $\mathbf{objgrd}\left(\mathbf{n}\right)$ – Real (Kind=nag_wp) array Input/Output

On entry: the elements of objgrd are set to special values which enable e05ucf to detect whether they are changed by objfun.

On exit: if $\mathbf{mode}=1$ or $2$, objgrd must return the available elements of the gradient evaluated at $x$.

6: $\mathbf{nstate}$ – Integer Input

On entry: if $\mathbf{nstate}=1$ then e05ucf is calling objfun for the first time on the current local optimization problem. This argument setting allows you to save computation time if certain data must be calculated only once.

7: $\mathbf{iuser}\left(*\right)$ – Integer array User Workspace

8: $\mathbf{ruser}\left(*\right)$ – Real (Kind=nag_wp) array User Workspace

objfun is called with the arguments iuser and ruser as supplied to e05ucf. You should use the arrays iuser and ruser to supply information to objfun.

objfun must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which e05ucf is called. Arguments denoted as Input must not be changed by this procedure.

Note: objfun should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by e05ucf. If your code inadvertently does return any NaNs or infinities, e05ucf is likely to produce unexpected results.

objfun should be tested separately before being used in conjunction with e05ucf. See also the description of the optional parameter Verify.

10: $\mathbf{npts}$ – Integer Input

On entry: the number of different starting points to be generated and used. The more points used, the more likely that the best returned solution will be a global minimum.

Constraint: $1\le \mathbf{nb}\le \mathbf{npts}$.

11: $\mathbf{x}(\mathbf{ldx},\mathbf{nb})$ – Real (Kind=nag_wp) array Output

On exit: $\mathbf{x}(\mathit{j},i)$ contains the final estimate of the $i$th solution, for $\mathit{j}=1,2,\dots ,\mathbf{n}$.

12: $\mathbf{ldx}$ – Integer Input

On entry: the first dimension of the array x as declared in the (sub)program from which e05ucf is called.

Constraint: $\mathbf{ldx}\ge \mathbf{n}$.

13: $\mathbf{start}$ – Subroutine, supplied by the NAG Library or the user. External Procedure

start must calculate the npts starting points to be used by the local optimizer. If you do not wish to write a routine specific to your problem then e05ucz may be used as the actual argument. e05ucz is supplied in the NAG Library and uses the NAG quasi-random number generators to distribute starting points uniformly across the domain. It is affected by the value of repeat.

The specification of start is:

Fortran Interface copy

Subroutine start ( npts, quas, n, repeat, bl, bu, iuser, ruser, mode) Integer, Intent (In) :: npts, n Integer, Intent (Inout) :: iuser(*), mode Real (Kind=nag_wp), Intent (In) :: bl(n), bu(n) Real (Kind=nag_wp), Intent (Inout) :: quas(n,npts), ruser(*) Logical, Intent (In) :: repeat

C Header Interface copy

void  start (const Integer *npts, double quas[], const Integer *n, const logical *repeat, const double bl[], const double bu[], Integer iuser[], double ruser[], Integer *mode)

C++ Header Interface copy

#include <nag.h> extern "C" { void  start (const Integer &npts, double quas[], const Integer &n, const logical &repeat, const double bl[], const double bu[], Integer iuser[], double ruser[], Integer &mode) }

1: $\mathbf{npts}$ – Integer Input

On entry: indicates the number of starting points.

2: $\mathbf{quas}(\mathbf{n},\mathbf{npts})$ – Real (Kind=nag_wp) array Input/Output

On entry: all elements of quas will have been set to zero, so only nonzero values need be set subsequently.

On exit: must contain the starting points for the npts local minimizations, i.e., $\mathbf{quas}(j,i)$ must contain the $j$th component of the $i$th starting point.

3: $\mathbf{n}$ – Integer Input

On entry: the number of variables.

4: $\mathbf{repeat}$ – Logical Input

On entry: specifies whether a repeatable or non-repeatable sequence of points are to be generated.

5: $\mathbf{bl}\left(\mathbf{n}\right)$ – Real (Kind=nag_wp) array Input

On entry: the lower bounds on the variables. These may be used to ensure that the starting points generated in some sense ‘cover’ the region, but there is no requirement that a starting point be feasible.

6: $\mathbf{bu}\left(\mathbf{n}\right)$ – Real (Kind=nag_wp) array Input

On entry: the upper bounds on the variables. (See bl.)

7: $\mathbf{iuser}\left(*\right)$ – Integer array User Workspace

8: $\mathbf{ruser}\left(*\right)$ – Real (Kind=nag_wp) array User Workspace

start is called with the arguments iuser and ruser as supplied to e05ucf. You should use the arrays iuser and ruser to supply information to start.

9: $\mathbf{mode}$ – Integer Input/Output

On entry: mode will contain $0$.

On exit: if you set mode to a negative value then e05ucf will terminate immediately with $\mathbf{ifail}=\mathbf{9}$.

start must either be a module subprogram USEd by, or declared as EXTERNAL in, the (sub)program from which e05ucf is called. Arguments denoted as Input must not be changed by this procedure.

Note: start should not return floating-point NaN (Not a Number) or infinity values, since these are not handled by e05ucf. If your code inadvertently does return any NaNs or infinities, e05ucf is likely to produce unexpected results.

14: $\mathbf{repeat}$ – Logical Input

On entry: is passed as an argument to start and may be used to initialize a random number generator to a repeatable, or non-repeatable, sequence.

15: $\mathbf{nb}$ – Integer Input

On entry: the number of solutions to be returned. The routine saves up to nb local minima ordered by increasing value of the final objective function. If the defining criterion for ‘best solution’ is only that the value of the objective function is as small as possible then nb should be set to $1$. However, if you want to look at other solutions that may have desirable properties then setting $\mathbf{nb}>1$ will produce nb local minima, ordered by increasing value of their objective functions at the minima.

Constraint: $1\le \mathbf{nb}\le \mathbf{npts}$.

16: $\mathbf{objf}\left(\mathbf{nb}\right)$ – Real (Kind=nag_wp) array Output

On exit: $\mathbf{objf}\left(i\right)$ contains the value of the objective function at the final iterate for the $i$th solution.

17: $\mathbf{objgrd}(\mathbf{ldobjd},\mathbf{nb})$ – Real (Kind=nag_wp) array Output

On exit: $\mathbf{objgrd}(\mathit{j},i)$ contains the gradient of the objective function for the $i$th solution at the final iterate (or its finite difference approximation), for $\mathit{j}=1,2,\dots ,\mathbf{n}$.

18: $\mathbf{ldobjd}$ – Integer Input

On entry: the first dimension of the array objgrd as declared in the (sub)program from which e05ucf is called.

Constraint: $\mathbf{ldobjd}\ge \mathbf{n}$.

19: $\mathbf{iter}\left(\mathbf{nb}\right)$ – Integer array Output

On exit: $\mathbf{iter}\left(i\right)$ contains the number of major iterations performed to obtain the $i$th solution. If less than nb solutions are returned then $\mathbf{iter}\left(\mathbf{nb}\right)$ contains the number of starting points that have resulted in a converged solution. If this is close to npts then this might be indicative that fewer than nb local minima exist.

20: $\mathbf{c}(\mathbf{ldc},\mathbf{nb})$ – Real (Kind=nag_wp) array Output

On exit: if $\mathbf{ncnln}>0$, $\mathbf{c}(\mathit{j},\mathit{i})$ contains the value of the $\mathit{j}$th nonlinear constraint function $c_{\mathit{j}}$ at the final iterate, for the $\mathit{i}$th solution, for $\mathit{j}=1,2,\dots ,\mathbf{ncnln}$.

If $\mathbf{ncnln}=0$, c is not referenced.

21: $\mathbf{ldc}$ – Integer Input

On entry: the first dimension of the array c as declared in the (sub)program from which e05ucf is called.

Constraint: $\mathbf{ldc}\ge \mathbf{ncnln}$.

22: $\mathbf{cjac}(\mathbf{ldcjac},\mathbf{sdcjac},\mathbf{nb})$ – Real (Kind=nag_wp) array Output

On exit: if $\mathbf{ncnln}>0$, cjac contains the Jacobian matrices of the nonlinear constraint functions at the final iterate for each of the returned solutions, i.e., $\mathbf{cjac}(\mathit{k},\mathit{j},i)$ contains the partial derivative of the $\mathit{k}$th constraint function with respect to the $\mathit{j}$th variable, for $\mathit{k}=1,2,\dots ,\mathbf{ncnln}$ and $\mathit{j}=1,2,\dots ,\mathbf{n}$, for the $i$th solution. (See the discussion of argument cjsl under confun.)

If $\mathbf{ncnln}=0$, cjac is not referenced.

23: $\mathbf{ldcjac}$ – Integer Input

On entry: the first dimension of the array cjac as declared in the (sub)program from which e05ucf is called.

Constraint: $\mathbf{ldcjac}\ge \mathbf{ncnln}$.

24: $\mathbf{sdcjac}$ – Integer Input

On entry: the second dimension of the array cjac as declared in the (sub)program from which e05ucf is called.

Constraint: if $\mathbf{ncnln}>0$, $\mathbf{sdcjac}\ge \mathbf{n}$.

25: $\mathbf{r}(\mathbf{ldr},\mathbf{sdr},\mathbf{nb})$ – Real (Kind=nag_wp) array Output

On exit: for each of the nb solutions r will contain a form of the Hessian; for the $i$th returned solution $\mathbf{r}(\mathbf{ldr},\mathbf{sdr},i)$ contains the Hessian that would be returned from the local minimizer. If $\mathbf{Hessian}=\mathrm{NO}$, the default, each $\mathbf{r}(\mathbf{ldr},\mathbf{sdr},i)$ contains the upper triangular Cholesky factor $R$ of $Q^{\mathrm{T}}HQ$, an estimate of the transformed and reordered Hessian of the Lagrangian at $x$. If $\mathbf{Hessian}=\mathrm{YES}$, $\mathbf{r}(\mathbf{ldr},\mathbf{sdr},i)$ contains the upper triangular Cholesky factor $R$ of $H$, the approximate (untransformed) Hessian of the Lagrangian, with the variables in the natural order.

26: $\mathbf{ldr}$ – Integer Input

On entry: the first dimension of the array r as declared in the (sub)program from which e05ucf is called.

Constraint: $\mathbf{ldr}\ge \mathbf{n}$.

27: $\mathbf{sdr}$ – Integer Input

On entry: the second dimension of the array r as declared in the (sub)program from which e05ucf is called.

Constraint: $\mathbf{sdr}\ge \mathbf{n}$.

28: $\mathbf{clamda}(\mathbf{ldclda},\mathbf{nb})$ – Real (Kind=nag_wp) array Output

On exit: the values of the QP multipliers from the last QP subproblem solved for the $i$th solution. $\mathbf{clamda}(j,i)$ should be non-negative if $\mathbf{istate}(j,i)=1$ and non-positive if $\mathbf{istate}(j,i)=2$.

29: $\mathbf{ldclda}$ – Integer Input

On entry: the first dimension of the array clamda as declared in the (sub)program from which e05ucf is called.

Constraint: $\mathbf{ldclda}\ge \mathbf{n}+\mathbf{nclin}+\mathbf{ncnln}$.

30: $\mathbf{istate}(\mathbf{listat},\mathbf{nb})$ – Integer array Output

On exit: $\mathbf{istate}(j,i)$ contains the status of the constraints in the QP working set for the $i$th solution. The significance of each possible value of $\mathbf{istate}(j,i)$ is as follows:

$\mathbf{istate}(\mathit{j},\mathit{i})$	Meaning
$\phantom{-}0$	The constraint is satisfied to within the feasibility tolerance, but is not in the QP working set.
$\phantom{-}1$	This inequality constraint is included in the QP working set at its lower bound.
$\phantom{-}2$	This inequality constraint is included in the QP working set at its upper bound.
$\phantom{-}3$	This constraint is included in the QP working set as an equality. This value of istate can occur only when $\mathbf{bl}\left(j\right)=\mathbf{bu}\left(j\right)$.

31: $\mathbf{listat}$ – Integer Input

On entry: the first dimension of the array istate as declared in the (sub)program from which e05ucf is called.

Constraint: $\mathbf{listat}\ge \mathbf{n}+\mathbf{nclin}+\mathbf{ncnln}$.

32: $\mathbf{iopts}\left(740\right)$ – Integer array Communication Array

33: $\mathbf{opts}\left(485\right)$ – Real (Kind=nag_wp) array Communication Array

The arrays iopts and opts must not be altered between calls to any of the routines e05ucf and e05zkf.

34: $\mathbf{iuser}\left(*\right)$ – Integer array User Workspace

35: $\mathbf{ruser}\left(*\right)$ – Real (Kind=nag_wp) array User Workspace

iuser and ruser are not used by e05ucf, but are passed directly to confun, objfun and start and may be used to pass information to these routines.

With care, you may also write information back into iuser and ruser. This might be useful, for example, should there be a need to preserve the state of a random number generator.

With SMP-enabled versions of e05ucf the arrays iuser and ruser provided are classified as OpenMP shared memory. Use of iuser and ruser has to take account of this in order to preserve thread safety whenever information is written back to either of these arrays.

36: $\mathbf{info}\left(\mathbf{nb}\right)$ – Integer array Output

On exit: $\mathbf{info}\left(i\right)$ contains one of $0$, $1$ or $6$. Please see the description of each corresponding value of ifail on exit from e04ucf/​e04uca for detailed explanations of these exit values. As usual $0$ denotes success.

If $\mathbf{ifail}=\mathbf{8}$ on exit, then not all nb solutions have been found, and $\mathbf{info}\left(\mathbf{nb}\right)$ contains the number of solutions actually found.

37: $\mathbf{ifail}$ – Integer Input/Output

On entry: ifail must be set to $0$, $\mathrm{-1}$ or $1$ to set behaviour on detection of an error; these values have no effect when no error is detected.

A value of $0$ causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of $\mathrm{-1}$ means that an error message is printed while a value of $1$ means that it is not.

If halting is not appropriate, the value $\mathrm{-1}$ or $1$ is recommended. If message printing is undesirable, then the value $1$ is recommended. Otherwise, the value $\mathrm{-1}$ is recommended since useful values can be provided in some output arguments even when $\mathbf{ifail}\ne \mathbf{0}$ on exit. When the value $-\mathbf{1}$ or $\mathbf{1}$ is used it is essential to test the value of ifail on exit.

On exit: $\mathbf{ifail}=\mathbf{0}$ unless the routine detects an error or a warning has been flagged (see Section 6).

6 Error Indicators and Warnings

$\mathbf{ifail}=1$

An input value is incorrect. One or more of the following constraints are violated.

• On entry, $\mathbf{bl}\left(i\right)>\mathbf{bu}\left(i\right)$: $i=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{bl}\left(i\right)\le \mathbf{bu}\left(i\right)$, for all $i$.
• On entry, $\mathbf{lda}=⟨\mathit{\text{value}}⟩$ and $\mathbf{nclin}=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{lda}\ge \mathbf{nclin}$.
• On entry, $\mathbf{ldc}=⟨\mathit{\text{value}}⟩$ and $\mathbf{ncnln}=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{ldc}\ge \mathbf{ncnln}$.
• On entry, $\mathbf{ldcjac}=⟨\mathit{\text{value}}⟩$ and $\mathbf{ncnln}=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{ldcjac}\ge \mathbf{ncnln}$.
• On entry, $\mathbf{ldclda}=⟨\mathit{\text{value}}⟩$, $\mathbf{n}=⟨\mathit{\text{value}}⟩$, $\mathbf{nclin}=⟨\mathit{\text{value}}⟩$ and $\mathbf{ncnln}=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{ldclda}\ge \mathbf{n}+\mathbf{nclin}+\mathbf{ncnln}$.
• On entry, $\mathbf{ldobjd}=⟨\mathit{\text{value}}⟩$ and $\mathbf{n}=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{ldobjd}\ge \mathbf{n}$.
• On entry, $\mathbf{ldr}=⟨\mathit{\text{value}}⟩$ and $\mathbf{n}=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{ldr}\ge \mathbf{n}$.
• On entry, $\mathbf{ldx}=⟨\mathit{\text{value}}⟩$ and $\mathbf{n}=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{ldx}\ge \mathbf{n}$.
• On entry, $\mathbf{listat}=⟨\mathit{\text{value}}⟩$, $\mathbf{n}=⟨\mathit{\text{value}}⟩$, $\mathbf{nclin}=⟨\mathit{\text{value}}⟩$ and $\mathbf{ncnln}=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{listat}\ge \mathbf{n}+\mathbf{nclin}+\mathbf{ncnln}$.
• On entry, $\mathbf{n}=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{n}>0$.
• On entry, $\mathbf{nb}=⟨\mathit{\text{value}}⟩$ and $\mathbf{npts}=⟨\mathit{\text{value}}⟩$.
  Constraint: $1\le \mathbf{nb}\le \mathbf{npts}$.
• On entry, $\mathbf{nclin}=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{nclin}\ge 0$.
• On entry, $\mathbf{ncnln}=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{ncnln}\ge 0$.
• On entry, $\mathbf{ncnln}>0$, $\mathbf{sdcjac}=⟨\mathit{\text{value}}⟩$ and $\mathbf{n}=⟨\mathit{\text{value}}⟩$.
  Constraint: if $\mathbf{ncnln}>0$, $\mathbf{sdcjac}\ge \mathbf{n}$.
• On entry, $\mathbf{sdr}=⟨\mathit{\text{value}}⟩$ and $\mathbf{n}=⟨\mathit{\text{value}}⟩$.
  Constraint: $\mathbf{sdr}\ge \mathbf{n}$.

$\mathbf{ifail}=2$

No solution obtained. Linear constraints may be infeasible.

e05ucf has terminated without finding any solutions. The majority of calls to the local optimizer have failed to find 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 (default value $\sqrt{\epsilon }$, where $\epsilon$ is the machine precision), 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 $\sigma$, you should ensure that the value of the optional parameter Linear Feasibility Tolerance is greater than $\sigma$. For example, if all elements of $A_L$ are of order unity and are accurate to only three decimal places, Linear Feasibility Tolerance should be at least ${10}^{\mathrm{-3}}$.

$\mathbf{ifail}=3$

e05ucf has failed to find any solutions. The majority of local optimizations could not find a feasible point for the nonlinear constraints. The problem may have no feasible solution. 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.)

No solution obtained. Nonlinear constraints may be infeasible.

$\mathbf{ifail}=4$

No solution obtained. Many potential solutions reach iteration limit.

The Iteration Limit may be changed using e05zkf.

$\mathbf{ifail}=7$

User-supplied derivatives probably wrong.

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 or enable 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 $x=0$ or $x=1$ are used to test function evaluation procedures, 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.

$\mathbf{ifail}=8$

Only $⟨\mathit{\text{value}}⟩$ solutions obtained.

Not all nb solutions have been found. $\mathbf{info}\left(\mathbf{nb}\right)$ contains the number actually found.

$\mathbf{ifail}=9$

User terminated computation from start procedure: $\mathbf{mode}=⟨\mathit{\text{value}}⟩$.

If e05ucz has been used as an actual argument for start then the message displayed, when $\mathbf{ifail}=\mathbf{0}$ or $-\mathbf{1}$ on entry to e05ucf, will have the following meaning:

$998$	failure to allocate space, a smaller value of NPTS should be tried.
$997$	an internal error has occurred. Please contact NAG for assistance.

$\mathbf{ifail}=10$

Failed to initialize optional parameter arrays.

$\mathbf{ifail}=-99$

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.

$\mathbf{ifail}=-399$

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.

$\mathbf{ifail}=-999$

Dynamic memory allocation failed.

See Section 9 in the Introduction to the NAG Library FL Interface for further information.

7 Accuracy

8 Parallelism and Performance

9 Further Comments

9.1 Description of the Printed Output

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

11 Algorithmic Details

12 Optional Parameters

12.1 Description of the Optional Parameters

13 Description of Monitoring Information