A Primal-Dual Regularized Interior-Point Method for Semidefinite Programming

Interior-point methods in semidefinite programming (SDP) require the solution of a sequence of linear systems which are used to derive the search directions. Safeguards are typically required in order to handle rank-deficient Jacobians and free variables. We generalize the primal-dual regularization of \cite{friedlander-orban-2012} to SDP and show that it is possible to recover an optimal … Read more

A matrix generation approach for eigenvalue optimization

We study the extension of a column generation technique to eigenvalue optimization. In our approach we utilize the method of analytic center to obtain the query points at each iteration. A restricted master problem in the primal space is formed corresponding to the relaxed dual problem. At each step of the algorithm, an oracle is … Read more

An interior point cutting plane method for convex feasibility problem with second-order cone inequalities

Convex feasibility problem in general, is a problem of finding a point in a convex set contains a full dimensional ball and is contained in a compact convex set. We assume that the outer set is described by second-order cone inequalities and propose an analytic center cutting plane technique to solve this problem. We discuss … Read more

The Integration of an Interior-Point Cutting-Plane Method within a Branch-and-Price Algorithm

This paper presents a novel integration of interior point cutting plane methods within branch-and-price algorithms. Unlike the classical method, columns are generated at a “central” dual solution by applying the analytic centre cutting plane method (ACCPM) on the dual of the full master problem. First, we introduce improvements to ACCPM. We propose a new procedure … Read more