optimize(algorithm=’nelder-mead’)

Model.optimize(mapping='uniform', algorithm=None, control_vector=None, bounds=None, jobs_fun='nse', wjobs_fun=None, event_seg=None, gauge='downstream', wgauge='mean', ost=None, options=None, verbose=True, inplace=False)

Optimize the Model using the Nelder-Mead algorithm.

See also

For documentation for the rest of the parameters, see Model.optimize

Options:

maxiter, maxfevint or None, default None

Maximum allowed number of iterations and function evaluations.

Note

If not given, default to N*200, where N is the number of variables, if neither maxiter or maxfev is set. If both maxiter and maxfev are set, minimization will stop at the first reached.

dispbool, default False

If True, display convergence messages.

return_allbool, default False

Set to True to return a list of the best solution at each of the iterations.

Warning

Not working at the moment.

initial_simplexnp.ndarray of shape (N + 1, N) or None, default None

Initial simplex.

Note

If given, overrides prior control. initial_simplex[j,:] should contain the coordinates of the jth vertex of the N+1 vertices in the simplex, where N is the dimension.

xatolfloat, default 0.0001

Absolute error in xopt between iterations that is acceptable for convergence.

fatolfloat, default 0.0001

Absolute error in func(xopt) between iterations that is acceptable for convergence.

adaptivebool, default True

Adapt algorithm parameters to dimensionality of problem. Useful for high-dimensional minimization.