embcol.optimizers.SLSQPLocalOptimizer¶

class embcol.optimizers.SLSQPLocalOptimizer(problem, callback=())[source]¶

SLSQP local optimizer.

Search a local optimum by the sequential least squares programming (SLSQP).

Parameters:

problem (embcol.problem.Problem) – Optimization problem.
callback (callable or sequence of callable, optional) – Function(s) called after each iteration. The signature of a function must be (params, cost, state) -> None, where params is a one-dimensional array of parameters of type numpy.ndarray, cost is a cost value of type float, and state is a dict object of str to float detailing an optimization state.

Inherited methods

optimize(init_oklabs[, max_n_iters])

Run an optimization.