NDLI: On self-regular IPMs

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Top : Volume 25

Top : Volume 24

Top : Volume 12, Issue 2, December 2004

On self-regular IPMs

Jordi Castro : Universitat Politècnica de Catalunya, Spain

Tibor Illés : Eötvös Loránd University of Sciences, Hungary

Florian Jarre : University of Düsseldorf, Germany

Arkadi Nemirovski : Technion — Israel Institute of Technology, Israel

Jiming Peng : McMaster University, Canada

Robert J. Vanderbei : Princeton University, USA

Rejoinder by M. Salahi, R. Sotirov and T. Terlaky

Two level hierarchical time minimizing transportation problem

On price competition in location-price models with spatially separated markets

Queues with postponed work

Various characterizations of convex fuzzy games

Finding economic order quantities with nonlinear goal programming

Top : Volume 12, Issue 1, June 2004

On self-regular IPMs

Content Provider	Springer Nature Link
Author	Salahi, Maziar Sotirov, Renata Terlaky, Tamás
Copyright Year	2004
Abstract	Primal-dual interior-point methods (IPMs) have shown their power in solving large classes of optimization problems. However, at present there is still a gap between the practical behavior of these algorithms and their theoretical worst-case complexity results, with respect to the strategies of updating the duality gap parameter in the algorithm. The so-called small-update IPMs enjoy the best known theoretical worst-case iteration bound, but work very poorly in practice. To the contrary, the so-called large-update IPMs have superior practical performance but with relatively weaker theoretical results. In this paper we discuss the new algorithmic variants and improved complexity results with respect to the new family of Self-Regular proximity based IPMs for Linear Optimization problems, and their generalizations to Conic and Semidefinite Optimization
Starting Page	209
Ending Page	275
Page Count	67
File Format	PDF
ISSN	11345764
Journal	Top
Volume Number	12
Issue Number	2
e-ISSN	18638279
Language	English
Publisher	Springer-Verlag
Publisher Date	2004-01-01
Publisher Institution	Spanish Society of Statistics and Operations Research
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Linear optimization semidefinite optimization conic optimization primal-dual interior-point method self-regular proximity function polynomial complexity Linear programming Semidefinite programming Interior-point methods Optimization Statistics for Business/Economics/Mathematical Finance/Insurance Industrial and Production Engineering Game Theory/Mathematical Methods Operations Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Discrete Mathematics and Combinatorics Management Science and Operations Research Information Systems and Management Modeling and Simulation

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The Complexity of Self-Regular Proximity Based Infeasible IPMs

A Polynomial Time Constraint-Reduced Algorithm for Semidefinite Optimization Problems

Generalization of Primal—Dual Interior-Point Methods to Convex Optimization Problems in Conic Form

A primal-dual interior-point algorithm for nonlinear least squares constrained problems

A new full-Newton step O(n) infeasible interior-point algorithm for semidefinite optimization

A primal–dual interior point method for nonlinear semidefinite programming

New complexity analysis for primal-dual interior-point methods for self-scaled optimization problems

An infeasible interior-point algorithm with full-Newton step for linear optimization

A polynomial primal-dual affine scaling algorithm for symmetric conic optimization

On self-regular IPMs

Similar Documents

The Complexity of Self-Regular Proximity Based Infeasible IPMs

A Polynomial Time Constraint-Reduced Algorithm for Semidefinite Optimization Problems

Generalization of Primal—Dual Interior-Point Methods to Convex Optimization Problems in Conic Form

A primal-dual interior-point algorithm for nonlinear least squares constrained problems

A new full-Newton step O(n) infeasible interior-point algorithm for semidefinite optimization

A primal–dual interior point method for nonlinear semidefinite programming

New complexity analysis for primal-dual interior-point methods for self-scaled optimization problems

An infeasible interior-point algorithm with full-Newton step for linear optimization

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On self-regular IPMs