hi all,

free (even for commercial purposes) solver interalg for global nonlinear optimization with specifiable accuracy (written in Python + NumPy) now can handle categorical variables, disjunctive (and other logical) constraints, thus making it available to solve GDP (Generalized Disjunctive Programming), possibly in multiobjective form.

Modern solvers, e.g. LogMIP, use Convex-Hull or Big-M algorithms for these nonlinear GDP, casting a GDP to series of MINLP, each one is usually solved by a sequence of (possibly nonconvex) NLP, while interalg uses absolutely different method and doesn't create any auxiliary variables and problems.

There are ~ 2 months till next OpenOpt release, but I guess someone may find it useful for his purposes right now.

See here for more details.

Regards, D.

asked 24 Apr '12, 09:18

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Dmitrey
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edited 07 Jul '12, 14:46

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fbahr ♦
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Asked: 24 Apr '12, 09:18

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Last updated: 07 Jul '12, 14:46

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