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MMP

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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:

Solver	License	Info
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.

Retrieved from "http://openopt.org/MMP"