Optimization Toolbox 4.1

Product Description

• Introduction and Key Features
• Defining, Solving, and Assessing Optimization Problems
• Nonlinear Optimization and Multi-Objective Optimization
• Nonlinear Least-Squares, Data Fitting, and Nonlinear Equations
• Quadratic, Linear, and Binary Integer Programming
• Solving Optimization Problems Using Parallel Computing

Solving Optimization Problems Using Parallel Computing

Optimization Toolbox can be used in conjunction with Parallel Computing Toolbox™ for solving 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 your optimization problem.

Built-in support for parallel computing allows you to accelerate the gradient estimation step in select solvers for constrained nonlinear optimization problems and multi-objective goal attainment and minimax problems.

Customizable support for parallel computing involves explicitly defining the optimization problem to use parallel computing functionality. You can define either your objective function or constraint function to use parallel computing, allowing you to decrease the time required to evaluate the objective/constraint.