Displaying similar documents to “Kernel-function Based Algorithms for Semidefinite Optimization”

Kernel-function Based Primal-Dual Algorithms for () Linear Complementarity Problems

M. EL Ghami, T. Steihaug (2010)

RAIRO - Operations Research

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Recently, [Y.Q. Bai, M. El Ghami and C. Roos, SIAM J. Opt. (2004) 101–128] investigated a new class of kernel functions which differs from the class of self-regular kernel functions. The class is defined by some simple conditions on the growth and the barrier behavior of the kernel function. In this paper we generalize the analysis presented in the above paper for () Linear Complementarity Problems (LCPs). The analysis for LCPs deviates significantly from the analysis for...

A Polynomial-time Interior-point Algorithm for Convex Quadratic Semidefinite Optimization

Y. Q. Bai, F. Y. Wang, X. W. Luo (2010)

RAIRO - Operations Research

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In this paper we propose a primal-dual interior-point algorithm for convex quadratic semidefinite optimization problem. The search direction of algorithm is defined in terms of a matrix function and the iteration is generated by full-Newton step. Furthermore, we derive the iteration bound for the algorithm with small-update method, namely, ( n log n ε ), which is best-known bound so far.

Generic Primal-dual Interior Point Methods Based on a New Kernel Function

M. EL Ghami, C. Roos (2008)

RAIRO - Operations Research

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In this paper we present a generic primal-dual interior point methods (IPMs) for linear optimization in which the search direction depends on a univariate kernel function which is also used as proximity measure in the analysis of the algorithm. The proposed kernel function does not satisfy all the conditions proposed in [2]. We show that the corresponding large-update algorithm improves the iteration complexity with a factor n 1 6 when compared with the method based on the use of...

Exact and heuristic approaches to solve the Internet shopping optimization problem with delivery costs

Mario C. Lopez-Loces, Jedrzej Musial, Johnatan E. Pecero, Hector J. Fraire-Huacuja, Jacek Blazewicz, Pascal Bouvry (2016)

International Journal of Applied Mathematics and Computer Science

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Internet shopping has been one of the most common online activities, carried out by millions of users every day. As the number of available offers grows, the difficulty in getting the best one among all the shops increases as well. In this paper we propose an integer linear programming (ILP) model and two heuristic solutions, the MinMin algorithm and the cellular processing algorithm, to tackle the Internet shopping optimization problem with delivery costs. The obtained results improve...

An adaptive long step interior point algorithm for linear optimization

Maziar Salahi (2010)

Kybernetika

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It is well known that a large neighborhood interior point algorithm for linear optimization performs much better in implementation than its small neighborhood counterparts. One of the key elements of interior point algorithms is how to update the barrier parameter. The main goal of this paper is to introduce an “adaptive” long step interior-point algorithm in a large neighborhood of central path using the classical logarithmic barrier function having O ( n log ( x 0 ) T s 0 ϵ ) iteration complexity analogous...