Global Optimization Toolbox

Global Search and Multistart Solvers

The global search and multistart solvers use gradient-based methods to return local and global minima. Both solvers start a local solver (in Optimization Toolbox) from multiple starting points and store local and global solutions found during the search process.

The global search solver:

• Uses a scatter-search algorithm to generate multiple starting points
• Filters nonpromising start points based upon objective and constraint function values and local minima already found
• Runs a constrained nonlinear optimization solver to search for a local minimum from the remaining start points

The multistart solver uses either uniformly distributed start points within predefined bounds or user-defined start points to find multiple local minima, including a single global minimum if one exists. The multistart solver runs the local solver from all starting points and can be run in serial or in parallel (using Parallel Computing Toolbox™). The multistart solver also provides flexibility in choosing different local nonlinear solvers. The available local solvers include unconstrained nonlinear, constrained nonlinear, nonlinear least-squares, and nonlinear least-squares curve fitting.

Nonlinear Optimization with the Global Search Solver 3:57
Local and global minima of the peaks function are found.

Nonlinear Regression with the Multistart Solver 4:16
The best-fit parameters for an exponential model are found.