Optimization with Semidefinite, Quadratic and Linear Constraints
Farid Alizadeh and Stefan Schmieta
We consider optimization problems where variables have either linear,
or convex quadratic or semidefinite constraints. First, we define and
characterize primal and dual nondegeneracy and strict complementarity
conditions. Next, we develop primal-dual interior point methods for
such problems and show that in the absence of degeneracy these
algorithms are numerically stable. Finally we describe an
implementation of our method and present numerical experiments with
both degenerate and nondegenerate problems.
RUTCOR, RRR Report 23-97, November 1997,
640 Bartholomew Rd, Piscataway NJ 08854.