Tamás Terlaky

On implementing a primal-dual interior-point method for conic quadratic optimization

Abstract. Based on the work of the Nesterov and Todd on self-scaled cones an implementation of a primal-dual interior-point method for solving large-scale sparse conic quadratic optimization problems is presented. The main features of the implementation are it is based on a homogeneous and self-dual model, it handles rotated quadratic cones directly, it employs a Mehrotra type predictor-corrector extension and sparse linear algebra to improve the computational efficiency. Finally, the implementation exploits fixed variables which naturally occurs in many conic quadratic optimization problems. This is a novel feature for our implementation. Computational results are also presented to document that the implementation can solve very large problems robustly and efficiently.

A Survey of the S-Lemma

In this survey we review the many faces of the S-lemma, a result about the correctness of the S-procedure. The basic idea of this widely used method came from control theory but it has important consequences in quadratic and semidefinite optimization, convex geometry, and linear algebra as well. These were all active research areas, but as there was little interaction between researchers in these different areas, their results remained mainly isolated. Here we give a unified analysis of the theory by providing three different proofs for the S-lemma and revealing hidden connections with various areas of mathematics. We prove some new duality results and present applications from control theory, error estimation, and computational geometry.

Self-regular functions and new search directions for linear and semidefinite optimization

Abstract.In this paper, we introduce the notion of a self-regular function. Such a function is strongly convex and smooth coercive on its domain, the positive real axis. We show that any such function induces a so-called self-regular proximity function and a corresponding search direction for primal-dual path-following interior-point methods (IPMs) for solving linear optimization (LO) problems. It is proved that the new large-update IPMs enjoy a polynomial ?(n

Self-regularity : a new paradigm for primal-dual interior-point algorithms

\frac{q+1}{2q}

On maximization of quadratic form over intersection of ellipsoids with common center

log

High Performance Optimization

\frac{n}{\varepsilon}

On Copositive Programming and Standard Quadratic Optimization Problems

) iteration bound, where q≥1 is the so-called barrier degree of the kernel function underlying the algorithm. The constant hidden in the ?-symbol depends on q and the growth degree p≥1 of the kernel function. When choosing the kernel function appropriately the new large-update IPMs have a polynomial ?(

Pivot rules for linear programming: A survey on recent theoretical developments

\sqrt{n}

SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING: JUST BE CAREFUL

lognlog

A convergent criss-cross method

\frac{n}{\varepsilon}