Sensitivity analysis in convex quadratic optimization: simultaneous perturbation of the objective and right-hand-side vectors

In this paper we study the behavior of Convex Quadratic Optimization problems when variation occurs simultaneously in the right-hand side vector of the constraints and in the coefficient vector of the linear term in the objective function. It is proven that the optimal value function is piecewise-quadratic. The concepts of transition point and invariancy interval … Read more

Towards a practical parallelisation of the simplex method

The simplex method is frequently the most efficient method of solving linear programming (LP) problems. This paper reviews previous attempts to parallelise the simplex method in relation to efficient serial simplex techniques and the nature of practical LP problems. For the major challenge of solving general large sparse LP problems, there has been no parallelisation … Read more

Some Disadvantages of a Mehrotra-Type Primal-Dual Corrector Interior Point Algorithm for Linear Programming

The Primal-Dual Corrector (PDC) algorithm that we propose computes on each iteration a corrector direction in addition to the direction of the standard primal-dual path-following interior point method (Kojima et al., 1989) for Linear Programming (LP), in an attempt to improve performance. The new iterate is chosen by moving along the sum of these directions, … Read more

A Full-Newton Step (n)$ Infeasible Interior-Point Algorithm for Linear Optimization

We present a full-Newton step infeasible interior-point algorithm. It is shown that at most $O(n)$ (inner) iterations suffice to reduce the duality gap and the residuals by the factor $\frac1{e}$. The bound coincides with the best known bound for infeasible interior-point algorithms. It is conjectured that further investigation will improve the above bound to $O(\sqrt{n})$. … Read more

Rigorous Error Bounds for the Optimal Value in Semidefinite Programming

A wide variety of problems in global optimization, combinatorial optimization as well as systems and control theory can be solved by using linear and semidefinite programming. Sometimes, due to the use of floating point arithmetic in combination with ill-conditioning and degeneracy, erroneous results may be produced. The purpose of this article is to show how … Read more

How good are interior point methods? Klee-Minty cubes tighten iteration-complexity bounds.

By refining a variant of the Klee-Minty example that forces the central path to visit all the vertices of the Klee-Minty n-cube, we exhibit a nearly worst-case example for path-following interior point methods. Namely, while the theoretical iteration-complexity upper bound is O(2^{n}n^{\frac{5}{2}}), we prove that solving this n-dimensional linear optimization problem requires at least $2^n-1$ … Read more

Computational Experience with Rigorous Error Bounds for the Netlib Linear Programming Library

The Netlib library of linear programming problems is a well known suite containing many real world applications. Recently it was shown by Ordonez and Freund that 71% of these problems are ill-conditioned. Hence, numerical difficulties may occur. Here, we present rigorous results for this library that are computed by a verification method using interval arithmetic. … Read more

A Stable Iterative Method for Linear Programming

This paper studies a new primal-dual interior/exterior-point method for linear programming. We begin with the usual perturbed primal-dual optimality equations $F_\mu(x,y,z)=0$. Under nondegeneracy assumptions, this nonlinear system is well-posed, i.e. it has a nonsingular Jacobian at optimality and is not necessarily ill-conditioned as the iterates approach optimality. We use a simple preprocessing step to eliminate … Read more

A New Complexity Result on Solving the Markov Decision Problem

We present a new complexity result on solving the Markov decision problem (MDP) with $n$ states and a number of actions for each state, a special class of real-number linear programs with the Leontief matrix structure. We prove that, when the discount factor $\theta$ is strictly less than $1$, the problem can be solved in … Read more

Faster approximation algorithms for packing and covering problems

We adapt a method due to Nesterov so as to obtain an algorithm for solving block-angular fractional packing or covering problems to relative tolerance epsilon, while using a number of iterations that grows polynomially in the size of the problem and whose dependency on epsilon is proportional to 1/epsilon. CitationCORC report TR-2004-09, Computational Optimization Research … Read more