scipy.optimize._minimize:minimize_scalar

Notes

This section describes the available solvers that can be selected by the 'method' parameter. The default method is the "Bounded" Brent method if bounds are passed and unbounded "Brent" otherwise.

Method Brent <optimize.minimize_scalar-brent> uses Brent's algorithm ^[1] to find a local minimum. The algorithm uses inverse parabolic interpolation when possible to speed up convergence of the golden section method.

Method Golden <optimize.minimize_scalar-golden> uses the golden section search technique ^[1]. It uses analog of the bisection method to decrease the bracketed interval. It is usually preferable to use the Brent method.

Method Bounded <optimize.minimize_scalar-bounded> can perform bounded minimization ^{[2] [3]}. It uses the Brent method to find a local minimum in the interval x1 < xopt < x2.

Note that the Brent and Golden methods do not guarantee success unless a valid bracket triple is provided. If a three-point bracket cannot be found, consider scipy.optimize.minimize. Also, all methods are intended only for local minimization. When the function of interest has more than one local minimum, consider global_optimization.

Custom minimizers

It may be useful to pass a custom minimization method, for example when using some library frontend to minimize_scalar. You can simply pass a callable as the method parameter.

The callable is called as method(fun, args, **kwargs, **options) where kwargs corresponds to any other parameters passed to minimize (such as bracket, tol, etc.), except the options dict, which has its contents also passed as method parameters pair by pair. The method shall return an OptimizeResult object.

The provided method callable must be able to accept (and possibly ignore) arbitrary parameters; the set of parameters accepted by minimize may expand in future versions and then these parameters will be passed to the method. You can find an example in the scipy.optimize tutorial.