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Using KNITRO with MATLAB
Introduction
KNITRO is a solver for non-liner optimization developed by ZIENA. This page provides information on how use the KNITRO solver with MATLAB on the FASRC cluster.
Using KNITRO
Currently, we have an active license for KNITRO version 9.1.0. The software is available with the knitro/9.1.0-fasrc01
module under LMOD and works with MATLAB version R2015a available with software module matlab/R2015a-fasrc01
. Below is a quick illustration on how to use the solver interactively:
(1) Start an interactive bash
shell:
[pkrastev@holy2a18308 ~]$
(2) Load appropriate software modules:
[pkrastev@holy2a18308 ~]$ module load knitro/9.1.0-fasrc01
(3) Start Matlab interactively and run a KNITRO test:
< M A T L A B (R) >
Copyright 1984-2015 The MathWorks, Inc.
R2015a (8.5.0.197613) 64-bit (glnxa64)
February 12, 2015
For online documentation, see http://www.mathworks.com/support
For product information, visit www.mathworks.com.
Academic License
>> [x fval] = knitromatlab(@(x)cos(x),1)
======================================
Academic Ziena License (NOT FOR COMMERCIAL USE)
KNITRO 9.1.0
Ziena Optimization
======================================
KNITRO presolve eliminated 0 variables and 0 constraints.
algorithm: 1
gradopt: 4
hessopt: 2
honorbnds: 1
maxit: 10000
outlev: 1
par_concurrent_evals: 0
The problem is identified as unconstrained.
KNITRO changing bar_switchrule from AUTO to 1.
KNITRO changing bar_murule from AUTO to 4.
KNITRO changing bar_initpt from AUTO to 3.
KNITRO changing bar_penaltyrule from AUTO to 2.
KNITRO changing bar_penaltycons from AUTO to 1.
KNITRO changing bar_switchrule from AUTO to 1.
KNITRO changing linsolver from AUTO to 2.
Problem Characteristics
———————–
Objective goal: Minimize
Number of variables: 1
bounded below: 0
bounded above: 0
bounded below and above: 0
fixed: 0
free: 1
Number of constraints: 0
linear equalities: 0
nonlinear equalities: 0
linear inequalities: 0
nonlinear inequalities: 0
range: 0
Number of nonzeros in Jacobian: 0
Number of nonzeros in Hessian: 1
EXIT: Locally optimal solution found.
Final Statistics
—————-
Final objective value = -1.00000000000000e+00
Final feasibility error (abs / rel) = 0.00e+00 / 0.00e+00
Final optimality error (abs / rel) = 2.37e-09 / 2.37e-09
# of iterations = 7
# of CG iterations = 0
# of function evaluations = 20
# of gradient evaluations = 0
Total program time (secs) = 0.44920 ( 0.251 CPU time)
Time spent in evaluations (secs) = 0.41352
===============================================================================
x =
3.1416
fval =
-1.0000
>>
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