Optimization in Scilab

May 11th, 2015
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Scilab provides a high-level matrix language and allows to define complex mathematical models and to easily connect to existing libraries. That is why optimization is an important and practical topic in Scilab, which provides tools to solve linear and nonlinear optimization problems by a large collection of tools

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Scilab OptimizationScilab DatasheetOptimization in ScilabScilab provides a high-level matrix language and allows to define complex mathematical models and to easily connect to existing libraries. That is why optimization is an important and practical topic in Scilab, which provides tools to solve linear and nonlinear optimization problems by a large collection of tools.Overview of the industrial-grade solvers available in Scilab and the type of optimization problems which can be solved by Scilab. Objective Linear Bounds y Equality l Inequalities l Problem size m m l l y Nonlinear s Gradient needed y n Solver linpro quapro qld qpsolve optim neldermead optim_ga fminsearch optim_sa lsqrsolve leastsq optim/"nd" optim_moga semidef lmisolveQuadraticylls Nonlinear Least Squares Min-Max Multi-Obj. Semi-Def. y y l*nl m s l* l* l loptional y n nFor the constraint columns, the letter "l" means linear, the letter "n" means nonlinear and "l*" means linear constraints in spectral sense. For the problem size column, the letters "s", "m" and "l" respectively mean small, medium and large.Focus on nonlinear optimizationw The optim function solves optimization problems with nonlinear objectives, with or without bound constraints on the unknowns. The quasi-Newton method optim/"qn" uses a Broyden-Fletcher-Goldfarb-Shanno formula to update the approximate Hessian matrix. The quasi-Newton method has a O(n) memory requirement. The limited memory BFGS algorithm opti

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