如何在scipy.optimize.basinhopping中禁用本地最小化过程？

请参阅“自定义最小化器”in the documentation of minimize()的讨论：

 **Custom minimizers**
It may be useful to pass a custom minimization method,for example
when using a frontend to this method such as `scipy.optimize.basinhopping`
or a different library. You can simply pass a callable as the ``method``
parameter.
The callable is called as ``method(fun,x0,args,**kwargs,**options)``
where ``kwargs`` corresponds to any other parameters passed to `minimize`
(such as `callback`,`hess`,etc.),except the `options` dict,which has
its contents also passed as `method` parameters pair by pair. Also,if
`jac` has been passed as a bool type,`jac` and `fun` are mangled so that
`fun` returns just the function values and `jac` is converted to a function
returning the Jacobian. 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.

基本上,您需要编写一个返回OptimizeResult的自定义函数,并通过minimizer_kwargs的方法部分将其传递给basinhopping,例如

from scipy.optimize import OptimizeResult
def noop_min(fun,**options):
    return OptimizeResult(x=x0,fun=fun(x0),success=True,nfev=1)

...

sol = basinhopping(...,minimizer_kwargs=dict(method=noop_min))

注意：我不知道跳过局部最小化如何影响流水线算法的收敛性.