LINEAR AND NON-LINEAR OPTIMISATION WITH MATLAB. LINEAR AND INTEGER PROGRAMMING

The Optimization Toolbox provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. The toolbox includes solvers for linear programming (LP), mixed-integer linear programming (MILP), quadratic programming (QP), second-order cone programming (SOCP), nonlinear programming (NLP), constrained linear least squares, nonlinear least squares, and nonlinear equations. You can define the optimization problem using functions and matrices, or by specifying variable expressions that reflect the underlying mathematics. You can use automatic differentiation of objective and constrained functions to obtain faster and more accurate solutions. You can use the toolbox solvers to find optimal solutions to continuous and discrete problems, perform tradeoff analysis, and incorporate optimization methods into algorithms and applications. The toolbox enables you to perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. This book delves into linear programming, nonlinear programming, and integer programming. It considers both problem-based optimization and solver-based optimization. A variety of exercises solved with MATLAB are presented.