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Finding a point in the relative interior of a polyhedron

Coralia Cartis(C.Cartis***at***rl.ac.uk)
Nicholas I M Gould(nimg***at***comlab.ox.ac.uk)

Abstract: A new initialization or `Phase I' strategy for feasible interior point methods for linear programming is proposed that computes a point on the primal-dual central path associated with the linear program. Provided there exist primal-dual strictly feasible points --- an all-pervasive assumption in interior point method theory that implies the existence of the central path --- our initial method (Algorithm 1) is globally Q-linearly and asymptotically Q-quadratically convergent, with a provable worst-case iteration complexity bound. When this assumption is not met, the numerical behaviour of Algorithm 1 is highly disappointing, even when the problem is primal-dual feasible. This is due to the presence of implicit equalities, inequality constraints that hold as equalities at all the feasible points. Controlled perturbations of the inequality constraints of the primal-dual problems are introduced --- geometrically equivalent to enlarging the primal-dual feasible region and then systematically contracting it back to its initial shape --- in order for the perturbed problems to satisfy the assumption. Thus Algorithm 1 can successfully be employed to solve each of the perturbed problems. We show that, when there exist primal-dual strictly feasible points of the original problems, the resulting method, Algorithm 2, finds such a point in a finite number of changes to the perturbation parameters. When implicit equalities are present, but the original problem and its dual are feasible, Algorithm 2 asymptotically detects all the primal-dual implicit equalities and generates a point in the relative interior of the primal-dual feasible set. Algorithm 2 can also asymptotically detect primal-dual infeasibility. Successful numerical experience with Algorithm 2 on linear programs from NETLIB and CUTEr, both with and without any significant preprocessing of the problems, indicates that Algorithm 2 may be used as an algorithmic preprocessor for removing implicit equalities, with theoretical guarantees of convergence.

Keywords: interior point methods, linear programming

Category 1: Linear, Cone and Semidefinite Programming (Linear Programming )

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: RAL Technical Report 2006-016, CCLRC Rutherford Appleton Laboratory, Chilton, Didcot, United Kingdom, OX11 0QX, December 2006. http://www.numerical.rl.ac.uk/reports/reports.shtml

Download: [Postscript][PDF]

Entry Submitted: 12/26/2006
Entry Accepted: 12/26/2006
Entry Last Modified: 12/26/2006

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