# NAG FL Interfacee04raf (handle_​init)

## 1Purpose

e04raf initializes a data structure for the NAG optimization modelling suite for problems such as, linear programming (LP), quadratic programming (QP), nonlinear programming (NLP), least squares (LSQ) problems, second-order cone programming (SOCP), linear semidefinite programming (SDP) and semidefinite programming with bilinear matrix inequalities (BMI-SDP).

## 2Specification

Fortran Interface
 Subroutine e04raf ( nvar,
 Integer, Intent (In) :: nvar Integer, Intent (Inout) :: ifail Type (c_ptr), Intent (Out) :: handle
#include <nag.h>
 void e04raf_ (void **handle, const Integer *nvar, Integer *ifail)
The routine may be called by the names e04raf or nagf_opt_handle_init.

## 3Description

e04raf initializes an empty problem with $n$ decision variables, $x$, and returns a handle to the data structure. This handle may then be passed to some of the routines e04rbf, e04ref, e04rff, e04rgf, e04rhf, e04rjf, e04rkf, e04rlf, e04rmf, e04rnf and e04rpf to formulate the problem (define the objective function and constraints) and to a compatible solver, e04fff, e04fgf, e04jdf, e04jef, e04kff, e04mtf, e04ptf, e04stf or e04svf, to solve it. The handle must not be changed between calls. When the handle is no longer needed, e04rzf must be called to destroy it and deallocate all the allocated memory and data within. See Section 3.1 in the E04 Chapter Introduction for more details about the NAG optimization modelling suite.

None.

## 5Arguments

1: $\mathbf{handle}$Type (c_ptr) Output
Note: handle does not need to be set on input.
On exit: holds a handle to the internal data structure where an empty problem with nvar variables is defined. You must not change the handle until the call to e04rzf (deallocation).
2: $\mathbf{nvar}$Integer Input
On entry: $n$, the number of decision variables in the problem.
Constraint: ${\mathbf{nvar}}>0$.
3: $\mathbf{ifail}$Integer Input/Output
On entry: ifail must be set to $0$, . 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 $1$ is recommended. Otherwise, if you are not familiar with this argument, the recommended value is $0$. When the value 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).

## 6Error Indicators and Warnings

If on entry ${\mathbf{ifail}}=0$ or $-1$, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
${\mathbf{ifail}}=6$
On entry, ${\mathbf{nvar}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{nvar}}>0$.
${\mathbf{ifail}}=-99$
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.

Not applicable.

## 8Parallelism and Performance

e04raf is not threaded in any implementation.

None.

## 10Example

See examples associated with other routines in the suite, such as:
• the examples in Section 10 in e04fff and Section 10 in e04fgf present a data fitting problem solved by a DFO LSQ solver,
• the examples in Section 10 in e04jdf and Section 10 in e04jef demonstrate how to use a DFO NLP solver,
• the example in Section 10 in e04kff solves a box-constrained nonlinear problem with a first-order solver,
• the example in Section 10 in e04mtf solves a small LP example using an LP IPM solver,
• the example in Section 10 in e04ptf solves a small convex QCQP problem reformulated as SOCP,
• the example in Section 10 in e04rdf demonstrates how to use the SDPA file reader and how to solve linear semidefinite programming problems, including printing of the matrix Lagrangian multipliers,
• the example in Section 10 in e04rff presents an alternative way to compute the nearest correlation matrix by means of nonlinear semidefinite programming,
• a matrix completion problem (minimization of a rank of a partially unknown matrix) formulated as SDP is demonstrated in Section 10 in e04rhf, the example also demonstrates the monitoring mode of the solver e04svf,
• the example in Section 10 in e04rjf solves LP/QP problems read in from an MPS file by e04mxf,
• an application for statistics, $E$ optimal design, solved as an SDP problem is shown in Section 10 in e04rnf,
• the example in Section 10 in e04rpf reads a BMI-SDP problem from a file which can be modified, in this case it solves a Static Output Feedback (SOF) problem,
• the example in Section 10 in e04rxf demonstrates how an approximate solution can be extracted during a monitoring step of e04mtf,
• the example in Section 10 in e04ryf walks through the life cycle of the handle in which a BMI-SDP problem is formulated and solved,
• an example in Section 10 in e04stf is a small test from Hock and Schittkowski set to show how to call the NLP solver,
• the simple example in Section 10 in e04svf demonstrates on the Lovász $\vartheta$ function eigenvalue optimization problem formulated as SDP.