NSP

Non-Smooth Problems (NSP)

$\mathbf {f(x) \to min,\ max}$

subjected to

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

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

$\mathbf{A}_\mathbf{eq} \mathbf{x} = \mathbf{b}_\mathbf{eq}$

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

$\mathbf{\forall j=0,...,J: h_j(x) = 0}$

$\mathbf{ \{ {f, c_i, h_j :R^n \to R \} \subset C^0}}$

(continuous functions,

sometimes with some numerical noise)

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

NSP solvers

Solver	License	Made by	Info
ralg	BSD	Dmitrey	For medium-scaled problems (nVars = 1...1000); ill-conditioned, piecewise linear and polynomial, non-smooth & noisy ones are also allowed. All types of constraints (lb <= x <= ub, Ax <= b, Aeqx = beq, c(x) <= 0, h(x) = 0). r-algorithm with adaptive space dilation had been invented by Ukrainian academician Naum Z. Shor (my teacher //Dmitrey). This solver is enhanced from time to time (almost each OpenOpt release).
ShorEllipsoid	BSD	Dmitrey	currently it's a tentative implementation of Naum Z. Shor method of ellipsoids; it's unconstrained, for small-scale problems with nVars = 1..10, requires r0: norm(x0-x*)<r0)	No
scipy_fmin	BSD		an implementation of Nelder-Mead simplex algorithm; unconstrained, cannot handle user-supplied derivatives
(coming) sbplx	LGPL	Steven G. Johnson.	A variant of Nelder-Mead algorithm. Requires nlopt installed.