OPTIMIZATION WITH MATLAB. QUADRATIC PROGRAMMING, LEAST SQUARES, SYSTEMS OF EQUATIONS, PROBLEM-BASED and BIG DATA for OPTIMIZATION

By J Lopez

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), nonlinear programming (NLP), constrained linear least squares, nonlinear least squares, and nonlinear equations. You can define your optimization problem with functions and matrices or by specifying variable expressions that reflect the underlying mathematics. You can use the toolbox solvers to fin optimal solutions to continuous and discrete problems, perform trade of analyses, and incorporate optimization methods into algorithms and applications. The toolbox lets you perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. It can be used to fin optimal solutions in applications such as portfolio optimization, resource allocation, and production planning and scheduling.You can use the toolbox solvers to find optimal solutions to continuous and discrete problems, perform tradeoff analyses, and incorporate optimization methods into algorithms and applications. The toolbox lets you perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. It can be used to find optimal solutions in applications such as portfolio optimization, resource allocation, and production planning and scheduling.Quadratic programming is the problem of finding a vector x that minimizes a quadratic function, possibly subject to linear constraints.Least squares, in general, is the problem of finding a vector x that is a local minimizer to a function that is a sum of squares, possibly subject to some constraints. There are several Optimization Toolbox solvers available for various types of F(x) and various types of constraints.Given a set of n nonlinear functions Fi(x), where n is the number of components of the vector x, the goal of equation solving is to find a vector x that makes all Fi(x) = 0. fsolve attempts to solve systems of equations by minimizing the sum of squares of the components. If the sum of squares is zero, the system of equation is solved.Matlab also support Big Data for Optimization across parallel computing. Parallel computing is the technique of using multiple processors on a single problem. The reason to use parallel computing is to speed computations for Big Data. The following Optimization Toolbox solvers can automatically distribute the numerical estimation of gradients of objective functions and nonlinear constraint functions to multiple processors: fmincon, fminunc, fgoalattain, fminimax, fsolve, lsqcurvefit and lsqnonlin.

• Format: Paperback
• Language: English
• ISBN: 1080217711
• ISBN13: 9781080217717
• Release Date: January 1
• Publisher: Independently Published
• Length: 298 Pages
• Weight: 0.97 lbs.
• Dimensions: 0.7" x 6.0" x 9.0"