Interfaces: FL CL CPP AD PY MB

e04re: FL CL CPP AD PY MB

Namespace: nagcpp

Namespace: opt

Function: handle_set_linobj

NAG CPP Interface
nagcpp::opt::handle_set_linobj (e04re)

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NAG Library Manual, Mark 30.3

Interfaces: FL CL CPP AD PY MB

NAG CPP Interface Introduction

E04 (Opt) Chapter Contents

e04re: FL CL CPP AD PY MB

Namespace: nagcpp

Namespace: opt

Function: handle_set_linobj

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Exceptions and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

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1 Purpose

handle_set_linobj is a part of the NAG optimization modelling suite and defines or redefines the objective function of the problem to be linear.

2 Specification

#include "e04/nagcpp_e04re.hpp"
#include "e04/nagcpp_class_CommE04RA.hpp"

template <typename COMM, typename CVEC>

void function handle_set_linobj(COMM &comm, const CVEC &cvec, OptionalE04RE opt)

template <typename COMM, typename CVEC>

void function handle_set_linobj(COMM &comm, const CVEC &cvec)

3 Description

After the handle has been initialized (e.g., handle_init has been called), handle_set_linobj may be used to define the objective function of the problem as a linear function

c^{T} x

using a dense vector

c

. If the objective function has already been defined, it will be overwritten. If

c = 0

, any existing objective function is removed, no new one is added and the problem will be solved as a feasible point problem. It is recommended to use handle_set_quadobj if the vector

c

is sparse. e04tef (no CPP interface) may be used to set individual elements

c_{i}

of the linear objective.

This will typically be used for 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)

Second-order Cone Programming (SOCP)

\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) \\ x_{G^{i}} \in K^{m_{i}}, i = 1, \dots, r, & (d) \end{array}

(2)

linear Semidefinite Programming problems (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}

(3)

or SDP with bilinear matrix inequalities (BMI-SDP) where the objective function has only linear terms. See Section 3.1 in the E04 Chapter Introduction for more details about the NAG optimization modelling suite.

4 References

None.

5 Arguments

1: $comm$ – CommE04RA Input/Output: Communication structure. An object of either the derived class CommE04RA or its base class NoneCopyableComm can be supplied. It is recommended that the derived class is used. If the base class is supplied it must first be initialized via a call to opt::handle_init (e04ra).
2: $cvec (nvar)$ – double array Input: On entry: the dense vector $c$ of the objective function.
3: $opt$ – OptionalE04RE Input/Output: Optional parameter container, derived from Optional.

5.1Additional Quantities

1: $nvar$: $n$ , the current number of decision variables $x$ in the model.

6 Exceptions and Warnings

Errors or warnings detected by the function:

All errors and warnings have an associated numeric error code field, errorid, stored either as a member of the thrown exception object (see errorid), or as a member of opt.ifail, depending on how errors and warnings are being handled (see Error Handling for more details).

Raises: ErrorException

$errorid = 1$: comm::handle has not been initialized.

$errorid = 1$: comm::handle does not belong to the NAG optimization modelling suite,
has not been initialized properly or is corrupted.

$errorid = 1$: comm::handle has not been initialized properly or is corrupted.

$errorid = 2$: The problem cannot be modified right now, the solver is running.

$errorid = 4$: On entry, $nvar = ⟨ value ⟩$ ,
expected $value = ⟨ value ⟩$ .
Constraint: $nvar$ must match the current number of variables
of the model in the comm::handle.

$errorid = 10601$: On entry, argument $⟨ value ⟩$ must be a vector of size $⟨ value ⟩$ array.
Supplied argument has $⟨ value ⟩$ dimensions.

$errorid = 10601$: On entry, argument $⟨ value ⟩$ must be a vector of size $⟨ value ⟩$ array.
Supplied argument was a vector of size $⟨ value ⟩$ .

$errorid = 10601$: On entry, argument $⟨ value ⟩$ must be a vector of size $⟨ value ⟩$ array.
The size for the supplied array could not be ascertained.

$errorid = 10602$: On entry, the raw data component of $⟨ value ⟩$ is null.

$errorid = 10603$: On entry, unable to ascertain a value for $⟨ value ⟩$ .

$errorid = 10605$: On entry, the communication class $⟨ value ⟩$ has not been initialized correctly.

$errorid = −99$: An unexpected error has been triggered by this routine.

$errorid = −399$: Your licence key may have expired or may not have been installed correctly.

$errorid = −999$: Dynamic memory allocation failed.

7 Accuracy

Not applicable.

8 Parallelism and Performance

Please see the description for the underlying computational routine in this section of the FL Interface documentation.

9 Further Comments

10 Example

Examples of the use of this method may be found in the examples for: handle_set_group and handle_solve_ipopt.

NAG Library Manual, Mark 30.3

Interfaces: FL CL CPP AD PY MB

NAG CPP Interface Introduction

E04 (Opt) Chapter Contents

e04re: FL CL CPP AD PY MB

Namespace: nagcpp

Namespace: opt

Function: handle_set_linobj

NAG CPP Interfacenagcpp::opt::handle_set_linobj (e04re)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

5.1Additional Quantities

6 Exceptions and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

NAG CPP Interface
nagcpp::opt::handle_set_linobj (e04re)