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Computational Experience of an Interior-Point SQP Algorithm in a Parallel Branch-and-Bound Framework
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@MISC{Lee_computationalexperience,
author = {Eva K. Lee and John E. Mitchell},
title = {Computational Experience of an Interior-Point SQP Algorithm in a Parallel Branch-and-Bound Framework },
year = {}
}
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Abstract
An interior-point algorithm within a parallel branch-and-bound framework for solving nonlinear mixed integer programs is described. The nonlinear programming relaxations at each node are solved using an interior point SQP method. In contrast to solving the relaxation to optimality at each tree node, the relaxation is only solved to near-optimality. Analogous to employing advanced bases in simplex-based linear MIP solvers, a “dynamic” collection of warmstart vectors is kept to provide “advanced warmstarts” at each branch-and-bound node. The code has the capability to run in both shared-memory and distributed-memory parallel environments. Preliminary computational results on various classes of linear mixed integer programs and quadratic portfolio problems are presented.
Keyphrases
parallel branch-and-bound framework interior-point sqp algorithm computational experience linear mixed integer program advanced warmstarts quadratic portfolio problem nonlinear programming relaxation dynamic collection nonlinear mixed integer program interior-point algorithm branch-and-bound node preliminary computational result various class advanced base tree node simplex-based linear mip solver distributed-memory parallel environment warmstart vector interior point sqp method
