Use simulated annealing when other solvers don't satisfy you.
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Basic example minimizing a function in the problem-based approach.
Presents an example of solving an optimization problem using simulated annealing.
This example shows how to create and minimize an objective function using the
simulannealbnd solver. It also shows how to include extra
parameters for the minimization.
Shows the effects of some options on the simulated annealing solution process.
Uses a custom data type to code a scheduling problem. Uses a custom plot function to monitor the optimization process.
Explains how to obtain identical results by setting the random seed.
Describes cases where hybrid functions are likely to provide greater accuracy or speed.
Introduces simulated annealing.
Explains some basic terminology for simulated annealing.
Presents an overview of how the simulated annealing algorithm works.
Explore the options for simulated annealing.