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NAG Library Function Document

nag_opt_handle_set_quadobj (e04rfc)

▸▿ 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

1 Purpose

nag_opt_handle_set_quadobj (e04rfc) is a part of the NAG optimization modelling suite and defines the linear or the quadratic objective function of the problem.

2 Specification

#include <nag.h>

#include <nage04.h>

void	nag_opt_handle_set_quadobj (void handle, Integer nnzc, const Integer idxc[], const double c[], Integer nnzh, const Integer irowh[], const Integer icolh[], const double h[], NagError fail)

3 Description

After the initialization function nag_opt_handle_init (e04rac) has been called, nag_opt_handle_set_quadobj (e04rfc) may be used to define the objective function of the problem as a quadratic function

c^{T} x + \frac{1}{2} x^{T} H x

or a sparse linear function

c^{T} x

unless the objective function has been defined previously by nag_opt_handle_set_linobj (e04rec), nag_opt_handle_set_quadobj (e04rfc) or by nag_opt_handle_set_nlnobj (e04rgc). This objective function will typically be used for quadratic programming problems (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}

(1)

or for semidefinite programming problems with bilinear matrix inequalities (BMI-SDP)

\begin{array}{l} \underset{x \in ℝ^{n}}{minimize} & \frac{1}{2} x^{T} H x + c^{T} x & (a) \\ subject to & \sum_{i,j = 1}^{n} x_{i} x_{j} Q_{i j}^{k} + \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}

(2)

The matrix

H

is a sparse symmetric

n

n

matrix. It does not need to be positive definite. See nag_opt_handle_init (e04rac) for more details.

4 References

None.

5 Arguments

1: $handle$ – void *Input

On entry: the handle to the problem. It needs to be initialized by nag_opt_handle_init (e04rac) and must not be changed.

2: $nnzc$ – IntegerInput

On entry: the number of nonzero elements in the sparse vector

c

nnzc = 0

c

is considered to be zero and the arrays idxc and c will not be referenced and may be NULL.

Constraint:

nnzc \geq 0

3: $idxc [nnzc]$ – const IntegerInput

4: $c [nnzc]$ – const doubleInput

On entry: the nonzero elements of the sparse vector

c

idxc [i - 1]

must contain the index of

c [i - 1]

in the vector, for

i = 1, 2, \dots, nnzc

. The elements are stored in ascending order. Note that

n

, the number of variables in the problem, was set in nvar during the initialization of the handle by nag_opt_handle_init (e04rac).

Constraints:

$1 \leq idxc [i - 1] \leq n$ , for $i = 1, 2, \dots, nnzc$ ;
$idxc [i - 1] < idxc [i]$ , for $i = 1, 2, \dots, nnzc - 1$ .

5: $nnzh$ – IntegerInput

On entry: the number of nonzero elements in the upper triangle of the matrix

H

nnzh = 0

, the matrix

H

is considered to be zero, the objective function is linear and irowh, icolh and h will not be referenced and may be NULL.

Constraint:

nnzh \geq 0

6: $irowh [nnzh]$ – const IntegerInput

7: $icolh [nnzh]$ – const IntegerInput

8: $h [nnzh]$ – const doubleInput

On entry: arrays irowh, icolh and h store the nonzeros of the upper triangle of the matrix

H

in coordinate storage (CS) format (see Section 2.1.1 in the f11 Chapter Introduction). irowh specifies one-based row indices, icolh specifies one-based column indices and h specifies the values of the nonzero elements in such a way that

h_{i j} = h [l - 1]

where

i = irowh [l - 1]

j = icolh [l - 1]

, for

l = 1, 2, \dots, nnzh

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

Constraint:

1 \leq irowh [l - 1] \leq icolh [l - 1] \leq n

, for

l = 1, 2, \dots, nnzh

9: $fail$ – NagError *Input/Output

The NAG error argument (see Section 2.7 in How to Use the NAG Library and its Documentation).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
NE_ALREADY_DEFINED: The objective function has already been defined.
NE_BAD_PARAM: On entry, argument $〈value〉$ had an illegal value.
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 nag_opt_handle_init (e04rac) or it has been corrupted.
NE_INT: On entry, $nnzc = 〈value〉$ .
Constraint: $nnzc \geq 0$ .

On entry, $nnzh = 〈value〉$ .
Constraint: $nnzh \geq 0$ .
NE_INTARR: On entry, $i = 〈value〉$ , $idxc [i - 1] = 〈value〉$ and $n = 〈value〉$ .
Constraint: $1 \leq idxc [i - 1] \leq n$ .
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.

An unexpected error has been triggered by this function. Please contact NAG.
See Section 2.7.6 in How to Use the NAG Library and its Documentation for further information.
NE_INVALID_CS: On entry, $i = 〈value〉$ , $icolh [i - 1] = 〈value〉$ and $n = 〈value〉$ .
Constraint: $1 \leq icolh [i - 1] \leq n$ .

On entry, $i = 〈value〉$ , $irowh [i - 1] = 〈value〉$ and $icolh [i - 1] = 〈value〉$ .
Constraint: $irowh [i - 1] \leq icolh [i - 1]$ (elements within the upper triangle).

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

On entry, more than one element of h has row index $〈value〉$ and column index $〈value〉$ .
Constraint: each element of h 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 2.7.5 in How to Use the NAG Library and its Documentation for further information.
NE_NOT_INCREASING: On entry, $i = 〈value〉$ , $idxc [i - 1] = 〈value〉$ and $idxc [i] = 〈value〉$ .
Constraint: $idxc [i - 1] < idxc [i]$ (ascending order).
NE_PHASE: The problem cannot be modified in this phase any more, the solver has already been called.

7 Accuracy

Not applicable.

8 Parallelism and Performance

nag_opt_handle_set_quadobj (e04rfc) is not threaded in any implementation.

9 Further Comments

None.

10 Example

This example demonstrates how to use nonlinear semidefinite programming to find a nearest correlation matrix satisfying additional requirements. This is a viable alternative to functions nag_nearest_correlation (g02aac), nag_nearest_correlation_bounded (g02abc), nag_nearest_correlation_h_weight (g02ajc) or nag_nearest_correlation_shrinking (g02anc) as it easily allows you to add further constraints on the correlation matrix. In this case a problem with a linear matrix inequality and a quadratic objective function is formulated to find the nearest correlation matrix in the Frobenius norm preserving the nonzero pattern of the original input matrix. However, additional box bounds (nag_opt_handle_set_simplebounds (e04rhc)) or linear constraints (nag_opt_handle_set_linconstr (e04rjc)) can be readily added to further bind individual elements of the new correlation matrix or new matrix inequalities (nag_opt_handle_set_linmatineq (e04rnc)) to restrict its eigenvalues.

The problem is as follows (to simplify the notation only the upper triangular parts are shown). To a given

m

m

symmetric input matrix

G

G = (\begin{matrix} g_{11} & \dots & g_{1 m} \\ ⋱ & ⋮ \\ g_{m m} \end{matrix})

find correction terms

x_{1}, \dots, x_{n}

which form symmetric matrix

\bar{G}

\bar{G} = (\begin{matrix} {\bar{g}}_{11} & {\bar{g}}_{12} & \dots & {\bar{g}}_{1 m} \\ {\bar{g}}_{22} & \dots & {\bar{g}}_{2 m} \\ ⋱ & ⋮ \\ {\bar{g}}_{m m} \end{matrix}) = (\begin{matrix} 1 & g_{12} + x_{1} & g_{13} + x_{2} & \dots & g_{1 m} + x_{i} \\ 1 & g_{23} + x_{3} \\ 1 & ⋮ \\ ⋱ \\ 1 & g_{m - 1 m} + x_{n} \\ 1 \end{matrix})

so that the following requirements are met:

(a)

It is a correlation matrix, i.e., symmetric positive semidefinite matrix with a unit diagonal. This is achieved by the way

\bar{G}

is assembled and by a linear matrix inequality

\begin{array}{l} \bar{G} = & x_{1} (\begin{matrix} 0 & 1 & 0 & \dots & 0 \\ 0 & 0 & \dots & 0 \\ 0 & \dots & 0 \\ ⋱ & ⋮ \\ 0 \end{matrix}) + x_{2} (\begin{matrix} 0 & 0 & 1 & \dots & 0 \\ 0 & 0 & \dots & 0 \\ 0 & \dots & 0 \\ ⋱ & ⋮ \\ 0 \end{matrix}) + x_{3} (\begin{matrix} 0 & 0 & 0 & \dots & 0 \\ 0 & 1 & \dots & 0 \\ 0 & \dots & 0 \\ ⋱ & ⋮ \\ 0 \end{matrix}) + \dots \\ + x_{n} (\begin{matrix} 0 & \dots & 0 & 0 & 0 \\ ⋱ & ⋮ & ⋮ & ⋮ \\ 0 & 0 & 0 \\ 0 & 1 \\ 0 \end{matrix}) - (\begin{matrix} - 1 & - g_{12} & - g_{13} & \dots & - g_{1 m} \\ - 1 & - g_{23} & \dots & - g_{2 m} \\ - 1 & \dots & - g_{3 m} \\ ⋱ & ⋮ \\ - 1 \end{matrix}) ⪰ 0 . \end{array}

(b)

\bar{G}

is nearest to

G

in the Frobenius norm, i.e., it minimizes the Frobenius norm of the difference which is equivalent to:

minimize ​ \frac{1}{2} \sum_{i \neq j} {({\bar{g}}_{i j} - g_{i j})}^{2} = \sum_{i = 1}^{n} x_{i}^{2} .

(c)

\bar{G}

preserves the nonzero structure of

G

. This is met by defining

x_{i}

only for nonzero elements

g_{i j}

For the input matrix

G = (\begin{matrix} 2 & - 1 & 0 & 0 \\ - 1 & 2 & - 1 & 0 \\ 0 & - 1 & 2 & - 1 \\ 0 & 0 & - 1 & 2 \end{matrix})

the result is

\bar{G} = (\begin{array}{r} 1.0000 & - 0.6823 & 0.0000 & 0.0000 \\ - 0.6823 & 1.0000 & - 0.5344 & 0.0000 \\ 0.0000 & - 0.5344 & 1.0000 & - 0.6823 \\ 0.0000 & 0.0000 & - 0.6823 & 1.0000 \end{array}) .

See also Section 10 in nag_opt_handle_init (e04rac) for links to further examples in the suite.

NAG Library Function Documentnag_opt_handle_set_quadobj (e04rfc)

▸▿ 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 Library Function Document

nag_opt_handle_set_quadobj (e04rfc)