SimulatedAnnealing

class wpimath.optimization.SimulatedAnnealing(initialTemperature: float, neighbor: Callable[[object], object], cost: Callable[[object], float])

Bases: pybind11_object

An implementation of the Simulated Annealing stochastic nonlinear optimization method.

Solving optimization problems involves tweaking decision variables to try to minimize some cost function. Simulated annealing is good for solving optimization problems with many local minima and a very large search space (it’s a heuristic solver rather than an exact solver like, say, SQP or interior-point method). Simulated annealing is a popular choice for solving the traveling salesman problem (see TravelingSalesman).

@see <a href=”https://en.wikipedia.org/wiki/Simulated_annealing”>https://en.wikipedia.org/wiki/Simulated_annealing</a> @tparam State The type of the state to optimize.

Constructor for Simulated Annealing that can be used for the same functions but with different initial states.

Parameters:

  • initialTemperature – The initial temperature. Higher temperatures make it more likely a worse state will be accepted during iteration, helping to avoid local minima. The temperature is decreased over time.

  • neighbor – Function that generates a random neighbor of the current state.

  • cost – Function that returns the scalar cost of a state.

solve(initialGuess: object, iterations: int) → object

Runs the Simulated Annealing algorithm.

Parameters:

  • initialGuess – The initial state.

  • iterations – Number of iterations to run the solver.

Returns:

The optimized state.