Save your money via IT outsourcing!

# MMP

MiniMax Problems (MMP)
$\mathbf{max_{k=0,...,K}\{f_k(x)\} \to min}$
subjected to
$\mathbf{lb} \le \mathbf{x} \le \mathbf{ub}$
$\mathbf{A x} \le \mathbf{b}$
$\mathbf{A}_\mathbf{eq} \mathbf{x} = \mathbf{b}_\mathbf{eq}$
$\mathbf{\forall i=0,...,I: c_i(x) \le 0}$
$\mathbf{\forall j=0,...,J: h_j(x) = 0}$
$\mathbf{ \{ {f, c_i, h_j :R^n \to R \} \subset C^1}}$
(smooth differentiable functions)
$\mathbf{x \in R^n}$

OpenOpt MMP example >>>

MMP solvers connected to OpenOpt:

converter to NLP example: r = p.solve('nlp:ipopt') (see the example). See NLP for list of the NLP solvers available.
nsmm BSD This one is quite primitive. Can handle linear & non-lin constraints, user-supplied gradients/subgradients df, dc, dh. The solver is intended to be enhanced in future. It just tries to solve NSP $max(f_k(x)) \to min$

For FuncDesigner problems you could consider using interalg - it can handle min and max functions.