void	e04rjc (void handle, Integer nclin, const double bl[], const double bu[], Integer nnzb, const Integer irowb[], const Integer icolb[], const double b[], Integer idlc, NagError *fail)

The function may be called by the names: e04rjc or nag_opt_handle_set_linconstr.

3 Description

After the initialization function e04rac has been called, e04rjc may be used to define the linear constraints

l_{B} \leq B x \leq u_{B}

of the problem unless the linear constraints have already been defined. This will typically be used for problems, such as linear programming (LP)

\begin{array}{l} \underset{x \in ℝ^{n}}{minimize} & c^{T} x & (a) \\ subject to & l_{B} \leq B x \leq u_{B}, & (b) \\ l_{x} \leq x \leq u_{x}, & (c) \end{array}

(1)

quadratic programming (QP)

\begin{array}{l} \underset{x \in ℝ^{n}}{minimize} & \frac{1}{2} x^{T} H x + c^{T} x & (a) \\ subject to & l_{B} \leq B x \leq u_{B}, & (b) \\ l_{x} \leq x \leq u_{x}, & (c) \end{array}

(2)

nonlinear programming (NLP)

\begin{array}{l} \underset{x \in ℝ^{n}}{minimize} & f (x) & (a) \\ subject to & l_{g} \leq g (x) \leq u_{g}, & (b) \\ l_{B} \leq B x \leq u_{B}, & (c) \\ l_{x} \leq x \leq u_{x}, & (d) \end{array}

(3)

or linear semidefinite programming (SDP)

\begin{array}{l} \underset{x \in ℝ^{n}}{minimize} & c^{T} x & (a) \\ subject to & \sum_{i = 1}^{n} x_{i} A_{i}^{k} - A_{0}^{k} ⪰ 0, k = 1, \dots, m_{A}, & (b) \\ l_{B} \leq B x \leq u_{B}, & (c) \\ l_{x} \leq x \leq u_{x}, & (d) \end{array}

(4)

where

n

is the number of decision variables,

B

is a general

m_{B} \times n

rectangular matrix and

l_{B}

and

u_{B}

are

m_{B}

-dimensional vectors. Note that upper and lower bounds are specified for all the constraints. This form allows full generality in specifying various types of constraint. In particular, the

j

th constraint may be defined as an equality by setting

l_{j} = u_{j}

. If certain bounds are not present, the associated elements of

l_{B}

u_{B}

may be set to special values that are treated as

- \infty

+ \infty

. See the description of the optional parameter

Infinite Bound Size

which is common among all solvers in the suite. Its value is denoted as

bigbnd

further in this text. Note that the bounds are interpreted based on its value at the time of calling this function and any later alterations to

Infinite Bound Size

will not affect these constraints.

See Section 4.1 in the E04 Chapter Introduction for more details about the NAG optimization modelling suite.

4 References

None.

5 Arguments

1: $handle$ – void * Input

On entry: the handle to the problem. It needs to be initialized by e04rac and must not be changed between calls to the NAG optimization modelling suite.

2: $nclin$ – Integer Input

On entry:

m_{B}

, the number of linear constraints (number of rows of the matrix

B

nclin = 0

, no linear constraints will be defined and bl, bu, nnzb, irowb, icolb and b will not be referenced and may be NULL.

Constraint:

nclin \geq 0

3: $bl [nclin]$ – const double Input

4: $bu [nclin]$ – const double Input

On entry: bl and bu define lower and upper bounds of the linear constraints,

l_{B}

and

u_{B}

, respectively. To define the

j

th constraint as equality, set

bl [j - 1] = bu [j - 1] = β

, where

|β| < bigbnd

. To specify a nonexistent lower bound (i.e.,

l_{j} = - \infty

), set

bl [j - 1] \leq - bigbnd

; to specify a nonexistent upper bound, set

bu [j - 1] \geq bigbnd

Constraints:

$bl [j - 1] \leq bu [j - 1]$ , for $j = 1, 2, \dots, nclin$ ;
$bl [j - 1] < bigbnd$ , for $j = 1, 2, \dots, nclin$ ;
$bu [j - 1] > - bigbnd$ , for $j = 1, 2, \dots, nclin$ ;
if $bl [j - 1] = bu [j - 1]$ , $|bl [j - 1]| < bigbnd$ , for $j = 1, 2, \dots, nclin$ .

5: $nnzb$ – Integer Input

On entry: nnzb gives the number of nonzeros in matrix

B

Constraint: if

nclin > 0

nnzb > 0

6: $irowb [nnzb]$ – const Integer Input

7: $icolb [nnzb]$ – const Integer Input

8: $b [nnzb]$ – const double Input

On entry: arrays irowb, icolb and b store nnzb nonzeros of the sparse matrix

B

in coordinate storage (CS) format (see Section 2.1.1 in the F11 Chapter Introduction). The matrix

B

has dimensions

m_{B} \times n

, where

n

, the number of variables in the problem, was set in nvar during the initialization of the handle by e04rac. irowb specifies one-based row indices, icolb specifies one-based column indices and b specifies the values of the nonzero elements in such a way that

b_{i j} = b [l - 1]

where

i = irowb [l - 1]

and

j = icolb [l - 1]

, for

l = 1, 2, \dots, nnzb

. No particular order of elements is expected, but elements should not repeat.

Constraint:

1 \leq irowb [l - 1] \leq nclin

1 \leq icolb [l - 1] \leq n

, for

l = 1, 2, \dots, nnzb

9: $idlc$ – Integer * Input/Output

Note: idlc is reserved for future releases of the NAG Library.

On entry: if

idlc = 0

, new linear constraints are added to the problem definition. This is the only value allowed at the moment.

Constraint:

idlc = 0

On exit: the number of the last linear constraint added, thus nclin.

10: $fail$ – NagError * Input/Output

The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_ALREADY_DEFINED: A set of linear constraints has already been defined.
NE_BAD_PARAM: On entry, argument $〈value〉$ had an illegal value.
NE_BOUND: On entry, $j = 〈value〉$ , $bl [j - 1] = 〈value〉$ , $bigbnd = 〈value〉$ .
Constraint: $bl [j - 1] < bigbnd$ .

On entry, $j = 〈value〉$ , $bl [j - 1] = 〈value〉$ and $bu [j - 1] = 〈value〉$ .
Constraint: $bl [j - 1] \leq bu [j - 1]$ .

On entry, $j = 〈value〉$ , $bu [j - 1] = 〈value〉$ , $bigbnd = 〈value〉$ .
Constraint: $bu [j - 1] > - bigbnd$ .
NE_HANDLE: The supplied handle does not define a valid handle to the data structure for the NAG optimization modelling suite. It has not been initialized by e04rac or it has been corrupted.
NE_INT: On entry, $nclin = 〈value〉$ .
Constraint: $nclin \geq 0$ .

On entry, $nnzb = 〈value〉$ .
Constraint: $nnzb > 0$ .
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_INVALID_CS: On entry, $i = 〈value〉$ , $icolb [i - 1] = 〈value〉$ and $n = 〈value〉$ .
Constraint: $1 \leq icolb [i - 1] \leq n$ .

On entry, $i = 〈value〉$ , $irowb [i - 1] = 〈value〉$ and $nclin = 〈value〉$ .
Constraint: $1 \leq irowb [i - 1] \leq nclin$ .

On entry, more than one element of b has row index $〈value〉$ and column index $〈value〉$ .
Constraint: each element of b must have a unique row and column index.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_PHASE: The problem cannot be modified in this phase any more, the solver has already been called.
NE_REF_MATCH: On entry, $idlc = 〈value〉$ .
Constraint: $idlc = 0$ .

7 Accuracy

Not applicable.

8 Parallelism and Performance

e04rjc is not threaded in any implementation.

9 Further Comments

None.

10 Example

This example demonstrates how to use the MPS file reader e04mxc and this suite of functions to define and solve a QP problem. e04mxc uses a different output format to the one required by e04rjc, in particular, it uses the compressed column storage (CCS) (see Section 2.1.3 in the F11 Chapter Introduction) instead of the coordinate storage and the linear objective vector is included in the system matrix. Therefore a simple transformation is needed before calling e04rjc as demonstrated in the example program.

The data file stores the following problem:

minimize c^{T} x + \frac{1}{2} x^{T} H x subject to \begin{matrix} l_{B} & \leq B x & \leq u_{B}, \\ - 2 & \leq x & \leq 2, \end{matrix}

where

c = (\begin{matrix} - 4.0 \\ - 1.0 \\ - 1.0 \\ - 1.0 \\ - 1.0 \\ - 1.0 \\ - 1.0 \\ - 0.1 \\ - 0.3 \end{matrix}), H = (\begin{matrix} 2 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 \\ 1 & 2 & 1 & 1 & 1 & 0 & 0 & 0 & 0 \\ 1 & 1 & 2 & 1 & 1 & 0 & 0 & 0 & 0 \\ 1 & 1 & 1 & 2 & 1 & 0 & 0 & 0 & 0 \\ 1 & 1 & 1 & 1 & 2 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \end{matrix}),

B = (\begin{matrix} 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 4.0 \\ 1.0 & 2.0 & 3.0 & 4.0 & - 2.0 & 1.0 & 1.0 & 1.0 & 1.0 \\ 1.0 & - 1.0 & 1.0 & - 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 \end{matrix}),

l_{B} = (\begin{matrix} - 2.0 \\ - 2.0 \\ - 2.0 \end{matrix}) and u_{B} = (\begin{matrix} 1.5 \\ 1.5 \\ 4.0 \end{matrix}) .

The optimal solution (to five figures) is

x^{*} = {(2.0, - 0.23333, - 0.26667, - 0.3, - 0.1, 2.0, 2.0, - 1.7777, - 0.45555)}^{T} .

See also Section 10 in e04rac for links to further examples in this suite.

Interfaces: FL CL AD

NAG CL Interface Introduction

E04 (Opt) Chapter Contents

E04 (Opt) Chapter Introduction

e04rj: FL CL AD

NAG CL Interfacee04rjc (handle_​set_​linconstr)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG CL Interface
e04rjc (handle_set_linconstr)