Shows how to solve for the minimum of Rosenbrock's function using different solvers, with or without gradients.
Example of unconstrained nonlinear programming.
Example of unconstrained nonlinear programming including derivatives.
Example of nonlinear programming using some derivative information.
Using multiple processors for optimization.
Automatic gradient estimation in parallel.
Considerations for speeding optimizations.
Special considerations in optimizing simulations, black-box objective functions, or ODEs.
Minimizing a single objective function in n dimensions without constraints.
fminsearch takes to
minimize a function.
Describes optimization options.
Explains why solvers might not find the smallest minimum.
Lists published materials that support concepts implemented in the solver algorithms.