Computational Experience of an Interior-Point SQP
Algorithm in a Parallel Branch-and-Bound Framework
Eva K. Lee and John E. Mitchell
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.
Technical Report LEC 97-08,
Industrial and Systems Engineering,
Georgia Institute of Technology.
Contact: [email protected]
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