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minlpBB
The solver minlpBB solves
large, sparse or dense
mixed-integer linear,
quadratic and nonlinear programming problems.
Main features
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MINLPbb
implements a branch-and-bound algorithm searching a tree whose nodes
correspond to continuous nonlinearly constrained optimization problems.
The continuous problems are solved using filterSQP.
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MINLPbb
needs second order information for the objective function and the
nonlinear constraints.
Tomlab estimates any unknown derivatives, but the accuracy and convergence
may be worse in such cases.
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The dense and the sparse version of
MINLPbb
are compiled in two different Mex binaries,
making the two versions optimally efficient.
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MINLPbb
is integrated with the Tomlab graphical user interface
and the Tomlab driver routines.
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MINLPbb
may be used as subproblem solver in the Tomlab
environment.
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More information about
MINLPbb
is available at the
Sven Leyffer,
University of Dundee,
home pages.
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