Optimization Toolbox
Description
- Introduction and Key Features
- Defining, Solving, and Assessing Optimization Problems
- Nonlinear Programming
- Multiobjective Optimization
- Nonlinear Least-Squares, Data Fitting, and Nonlinear Equations
- Linear Programming
- Quadratic Programming
- Solving Optimization Problems Using Parallel Computing
Solving Optimization Problems Using Parallel Computing
Optimization Toolbox can be used with Parallel Computing Toolbox™ to solve problems that benefit from parallel computation. You can use parallel computing to decrease time to solution by enabling built-in parallel computing support or by defining a custom parallel computing implementation of an optimization problem.
Built-in support for parallel computing in Optimization Toolbox enables you to accelerate the gradient estimation step in select solvers for constrained nonlinear optimization problems and multiobjective goal attainment and minimax problems.
Accelerating time to solution for an electrostatics problem using the built-in support for parallel computing in a nonlinear programming solver. The built-in functionality is enabled by specifying the UseParallel option (left) for the objective (middle right) and constraint (bottom right) functions, with the solution shown in the top right.
You can customize a parallel computing implementation by explicitly defining the optimization problem to use parallel computing functionality. You can define either an objective function or a constraint function to use parallel computing, enabling you to decrease the time required to evaluate the objective or constraint.
Accelerating time to solution (top right) for a suspension system design (bottom left and bottom right) subject to uncertainty by customizing the objective function with a single line change in code (top left).
