I have a non-convex function of several parameters, that takes real values. I am looking for a good algorithm to find local minima. I am reasonably confident I can come up with descent starting points. I have some simple boundary conditions (parameters need to be positive)
I cannot have an analytical form of the Jacobian.
I looked into Nelder-Mead, but it is said to perform poorly in high dimensions (I have about 7 parameters for my function - do not know if that qualifies as high dimension), and also I could not find an implementation in python / scipy which has constraints.
Can anyone with optimization experience suggest some good optimizing algorithms to try?