Computational experience of an interior-point SQP algorithm in a parallel
branch-and-bound framework
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Authors:
Eva K. Lee
School of Industrial and Systems Engineering
Georgia Institute of Technology
Atlanta, GA 30332-0205
evakylee@isye.gatech.edu
John E. Mitchell
Department of Mathematical Sciences
Rensselaer Polytechnic Institute
Troy, NY 12180 USA
mitchj@rpi.edu
September 15, 1997. Revised: December, 1998.
Abstract:
An interior point algorithm within a parallel branch-and-bound framework
for solving nonlinear mixed integer programming problems 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 shard-memory and distributed-memory
parallel environments. Preliminary computational results on various classes
of linear mixed integer programs and quadratic portfolio problems are
presented.
Publication details:
Appeared as Chapter 13, pages 329-347, of High Performance Optimization,
edited by H. Frenk et al., Kluwer Academic Publishers, 2000.
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