optimization_in_scilab.pdf

Mps files and the quapro sif files and the cuter

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MPS files and the Quapro toolbox . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 8.2 SIF files and the CUTEr toolbox . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 9 Scilab Optimization Toolboxes 57 9.1 Quapro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 9.1.1 Linear optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 9.1.2 Linear quadratic optimization . . . . . . . . . . . . . . . . . . . . . . . . . 58 9.2 CUTEr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 9.3 The Unconstrained Optimization Problem Toolbox . . . . . . . . . . . . . . . . . 60 9.4 Other toolboxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 10 Missing optimization features in Scilab 63 2
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Conclusion 64 Bibliography 65 3
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Copyright c 2008-2010 - Consortium Scilab - Digiteo - Michael Baudin Copyright c 2008-2009 - Consortium Scilab - Digiteo - Vincent Couvert Copyright c 2008-2009 - INRIA - Serge Steer This file must be used under the terms of the Creative Commons Attribution-ShareAlike 3.0 Unported License: 4
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Introduction This document aims at giving Scilab users a complete overview of optimization features in Scilab. It is written for Scilab partners needs in OMD project (). The core of this document is an analysis of current Scilab optimization features. In the final part, we give a short list of new features which would be interesting to find in Scilab. Above all the embedded functionalities of Scilab itself, some contributions (toolboxes) have been written to improve Scilab capabilities. Many of these toolboxes are interfaces to optimization libraries, such as FSQP for example. In this document, we consider optimization problems in which we try to minimize a cost function f ( x ) with or without constraints. These problems are partly illustrated in figure 1 . Several properties of the problem to solve may be taken into account by the numerical algorithms : The unknown may be a vector of real values or integer values. The number of unknowns may be small (from 1 to 10 - 100), medium (from 10 to 100 - 1 000) or large (from 1 000 - 10 000 and above), leading to dense or sparse linear systems. There may be one or several cost functions (multi-objective optimization). The cost function may be smooth or non-smooth. There may be constraints or no constraints. The constraints may be bounds constraints, linear or non-linear constraints. The cost function can be linear, quadratic or a general non linear function. An overview of Scilab optimization tools is showed in figure 2 . In this document, we present the following optimization features of Scilab. nonlinear optimization with the optim function, quadratic optimization with the qpsolve function, nonlinear least-square optimization with the lsqrsolve function, semidefinite programming with the semidef function, genetic algorithms with the optim_ga function, simulated annealing with the optim_sa function, linear matrix inequalities with the lmisolver function, 5
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>100 Unknowns 10-100 Unknowns 1-10 Unknowns Non-linear Objective Quadratic Objective Linear Objective Non-linear Constraints Linear Constraints Bounds Constraints With Constraints Without Constraints Smooth Non Smooth One Objective Several Objectives Continuous Parameters Discrete Parameters Optimization Figure 1: Classes of optimization problems 6
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Figure 2: Scilab Optimization Tools 7
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reading of MPS and SIF files with the
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