Optimization Toolbox provides widely used algorithms for standard and large-scale optimization. These algorithms solve constrained and unconstrained continuous and discrete problems. The toolbox includes functions for linear programming, quadratic programming, binary integer programming, nonlinear optimization, nonlinear least squares, systems of nonlinear equations, and multiobjective optimization. You can use them to find optimal solutions, perform tradeoff analyses, balance multiple design alternatives, and incorporate optimization methods into algorithms and models.
Finding a local minimum of the peaks function using a gradient-based optimization solver from Optimization Toolbox.
Key Features
- Interactive tools for defining and solving optimization problems and monitoring solution progress
- Solvers for nonlinear and multiobjective optimization
- Solvers for nonlinear least squares, data fitting, and nonlinear equations
- Methods for solving quadratic and linear programming problems
- Methods for solving binary integer programming problems
- Parallel computing support in selected constrained nonlinear solvers
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