On the Complexity of Linear Programming

In this paper we show a simple treatment of the complexity of Linear Programming. We describe the short step primal-dual path following algorithm and show that it solves the linear programming problem

The largest step path following algorithm for monotone linear complementarity problems

Path-following algorithms take at each iteration a Newton step for approaching a point on the central path, in such a way that all the iterates remain in a given neighborhood of that path. This paper studies the case in which each iteration uses a pure Newton step with the largest possible reduction in complementarity measure (duality gap).This algorithm is known to converge superlinearly in objective values. We show that with the addition of a computationally trivial safeguard it achieves Q-quadratic convergence, and show that this behaviour cannot be proved by usual techniques for the original method.

Fast convergence of the simplified largest step path following algorithm

Each master iteration of a simplified Newton algorithm for solving a system of equations starts by computing the Jacobian matrix and then uses this matrix in the computation of p Newton steps: the first of these steps is exact, and the other are called "simplified".In this paper we apply this approach to a large step path following algorithm for monotone linear complementarity problems. The resulting method generates sequences of objective values (duality gaps) that converge to zero with Q-order p + 1 in the number of master iterations, and with a complexity of O(V~nL) iterations

Fast Convergence of the Simplified Largest Step Path Following Algorithm

Each master iteration of a simplified Newton algorithm for solving a system of equations starts by computing the Jacobian matrix and then uses this matrix in the computation of p Newton steps: the first of these steps is exact, and the other are called "simplified". In this paper we apply this approach to a large step path following algorithm for monotone linear complementarity problems. The resulting method generates sequences of objective values (duality gaps) that converge to zero with Q-order p + 1 in the number of master iterations, and with a complexity of O( p nL) iterations

Convergence of Interior Point Algorithms for the Monotone Linear Complementarity Problem

The literature on interior point algorithms shows impressive results related to the speed of convergence of the objective values, but very little is known about the convergence of the iterate sequences. This paper studies the horizontal linear complementarity problem, and derives general convergence properties for algorithms based on Newton iterations. This problem provides a simple and general framework for most existing primal-dual interior point methods. The conclusion is that most of the published algorithms of this kind generate convergent sequences. In many cases (whenever the convergence is not too fast in a certain sense), the sequences converge to the analytic center of the optimal face

Examples of ill-behaved central paths in convex optimization

This paper presents some examples of ill-behaved central paths in convex optimization. Some contain infinitely many fixed length central segments; others manifest oscillations with infinite variation. These central paths can be encountered even for infinitely differentiable data

ON THE LIMITING PROPERTIES OF THE AFFINE-SCALING DIRECTIONS

We study the limiting properties of the affine-scaling directions for linear programming problems. The worst-case angle between the affine-scaling directions and the objective function vector provides an interesting measure that has been very helpful in convergence analyses and in understanding the behaviour of various interior-point algorithms. We establish new relations between this measure and some other complexity measures which are used in the complexity analyses of algorithms for linear programming. We also provide a new characterization of the smallest large variable complexity measure of Ye

Fast convergence of the simplified largest step path following algorithm

Programme 5 - Traitement du signal, automatique et productique. Projet Programmation mathematiqueSIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : 14802 E, issue : a.1994 n.2433 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc