Alejandra Hidalgo - Academia.edu (original) (raw)
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In this document, we make an overview of optimization features in Scilab. The goal of this docume... more In this document, we make an overview of optimization features in Scilab. The goal of this document is to present all existing and non-existing features, such that a user who wants to solve a particular optimization problem can know what to look for. In the introduction, we analyse a classification of optimization problems. In the first chapter, we analyse the flagship of Scilab in terms of nonlinear optimization: the optim function. We analyse its features, the management of the cost function, the linear algebra and the management of the memory. Then we consider the algorithms which are used behind optim, depending on the type of algorithm and the constraints. In the remaining chapters, we present the algorithms available to solve quadratic problems, nonlinear least squares problems, semidefinite programming, genetic algorithms, simulated annealing and linear matrix inequalities. A chapter focus on optimization data files managed by Scilab, especially MPS and SIF files. Some optimization features are available in the form of toolboxes, the most important of which are the Quapro and CUTEr toolboxes. The final chapter is devoted to missing optimization features in Scilab.
In this document, we make an overview of optimization features in Scilab. The goal of this docume... more In this document, we make an overview of optimization features in Scilab. The goal of this document is to present all existing and non-existing features, such that a user who wants to solve a particular optimization problem can know what to look for. In the introduction, we analyse a classification of optimization problems. In the first chapter, we analyse the flagship of Scilab in terms of nonlinear optimization: the optim function. We analyse its features, the management of the cost function, the linear algebra and the management of the memory. Then we consider the algorithms which are used behind optim, depending on the type of algorithm and the constraints. In the remaining chapters, we present the algorithms available to solve quadratic problems, nonlinear least squares problems, semidefinite programming, genetic algorithms, simulated annealing and linear matrix inequalities. A chapter focus on optimization data files managed by Scilab, especially MPS and SIF files. Some optimization features are available in the form of toolboxes, the most important of which are the Quapro and CUTEr toolboxes. The final chapter is devoted to missing optimization features in Scilab.