LINEAR AND NON-LINEAR PROGRAMMING. EQUATIONS AND SYSTEMS. EXAMPLES WITH MATLAB (ePub)

This book introduces linear algebra, programming elements, optimization techniques and other tools in order of technical difficulty. The book includes algorithms for solving linear programming problems, multiobjective problems, non-linear minimization with boundary conditions and restrictions, minimax optimization, semi-infinitely constrained minimization and quadratic programming.

MATLAB Optimization Toolbox includes routines for solving linear and quadratic programming problems use an active-set method combined with imaging techniques. The routines provide a range of algorithms and linear research strategies. Linear search strategies are protected methods of quadratic and cubic interpolation and extrapolation. The toolbox implements the current state of the art in optimization algorithms. The main algorithms for non-limited minimization are the BFGS quasi-Newton method and the Nelder-Mead simplex direct search method. Quadratic programming with sequential variations (SQP) is used for minimization with boundary conditions, achievement of objectives and semi-infinitely constrained optimization. Non-linear least-squares problems are solved using the Gauss-Newton or Levenberg-Marquardt methods.