NLSP

Non-Linear System Problems (NLSP)

Solve set of non-linear equations

$\mathbf{F(x)=0}$

subjected to

$\mathbf{lb} \le \mathbf{x} \le \mathbf{ub}$

$\mathbf{A x} \le \mathbf{b}$

$\mathbf{\forall i=0,...,I: c_i(x) \le 0}$

$\mathbf{F: R^n \to R^n}$

$\mathbf{x \in R^n}$

NLSP solvers connected to OpenOpt

Solver	License	Constraints	Derivatives	Info
scipy_fsolve	BSD	None	df	it's a wrapper around MINPACK's hybrd and hybrj algorithms.
converter to NLP	BSD	all that NLP solver can handle	all that NLP solver can handle	Example: r = p.solve('nlp:ralg'). It tries to minimize norm(F, 2) till required ftol and contol will be reached.
nssolve	BSD	lb, ub, Aeq, A, c, h (example)	df, dc, dh	It tries to minimize norm(F, inf) till required ftol and contol will be reache. The one is primarily for nonsmooth and noisy funcs, uses NSP ralg solver and is intended to be enhanced from time to time, as well as ralg. ns- can be interpreted as NonSmooth or NoiSy or Naum Shor (Ukrainian academician, my teacher, r-algorithm inventor). If no gradient is supplied, splitting can benefit.

The ones below work very unstable and can't use user-provided gradient, at least for scipy 0.6.0

Maybe they will be enhanced in future by someone. See here for details