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.
Nonlinear and multiobjective optimization of smooth constrained and unconstrained problems
Solvers for nonlinear least squares, constrained linear least squares, data fitting, and nonlinear equations
Quadratic programming (QP) and linear programming (LP)
Mixed-integer linear programming (MILP)
Optimization modeling tools
Graphical monitoring of optimization progress
Gradient estimation acceleration (with Parallel Computing Toolbox™)