On Superlinear Convergence of Infeasible-Interior-Point Algorithms for Linearly Constrained Convex Programs (1996)
| Venue: | Computational Optimization and Applications |
| Citations: | 2 - 0 self |
BibTeX
@ARTICLE{Monteiro96onsuperlinear,
author = {Renato D.C. Monteiro and Fangjun Zhou},
title = {On Superlinear Convergence of Infeasible-Interior-Point Algorithms for Linearly Constrained Convex Programs},
journal = {Computational Optimization and Applications},
year = {1996},
volume = {8}
}
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Abstract
This note derives bounds on the length of the primal-dual affine scaling directions associated with a linearly constrained convex program satisfying the following conditions: 1) the problem has a solution satisfying strict complementarity, 2) the Hessian of the objective function satisfies a certain invariance property. We illustrate the usefulness of these bounds by establishing the superlinear convergence of the algorithm presented in Wright and Ralph [22] for solving the optimality conditions associated with a linearly constrained convex program satisfying the above conditions. 1 Introduction During the past few years, we have seen the appearance of many papers dealing with primal-dual (feasible and infeasible) interior point algorithms for linear programs (LP), convex quadratic programs (QP), monotone linear complementarity problems (LCP) and monotone nonlinear complementarity problems (NCP) that are superlinearly or quadratically convergent. For LP and QP, these works include [1,...
