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 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.
Solve linear and nonlinear least-squares problems, data fitting problems, and nonlinear equations.Learn more
Discover more about Optimization Toolbox by exploring these resources.
Explore documentation for Optimization Toolbox functions and features, including release notes and examples.
Browse the list of available Optimization Toolbox functions.
View system requirements for the latest release of Optimization Toolbox.
Optimization Toolbox requires: MATLAB
Use Optimization Toolbox to solve scientific and engineering challenges: