New NLSP solver is available: nssolve, designed primarily for non-smooth and noisy funcs (scipy_fsolve usually is much more better with smooth funcs than nssolve).
This one is based on minimizing max residual via a NSP solver (default: ralg), can handle user-supplied gradient/subgradient. If the one is not available - splitting equations to separate functions is recommended (to speedup nssolve calculations):
f = [func1, func2, ...] or f = (func1, func2, ...)
f = lambda x: ...
def f(x): ...)
OO native graphical output is available
(w/o using p.connectIterFcn(..) like it is implemented for scipy_fsolve. Just set p.plot = True or p.plot = 1):
Pictures like the one below are automatically generated by the py-file
This example shows results of nssolve vs scipy.optimize fsolve with a little numerical noise (1e-8) . Here both nssolve and fsolve obtain same gradients from OO Kernel with p.diffInt = 1e-7 (default). ns- can be interpreted as
or Naum Shor (Ukrainian academician, my teacher, r-algorithm (implemented in openopt ralg) inventor)
Currently the nssolve is much far from recent nonsmooth solve algorithms (even those ones from our department), but it's better than having nothing at all, and I intend to enhance both r-alg and nssolve from time to time.