Corvinus
Corvinus

A new Ai-Zhang type interior point algorithm for sufficient linear complementarity problems

E. Nagy, Marianna and Varga, Anita (2022) A new Ai-Zhang type interior point algorithm for sufficient linear complementarity problems. Working Paper. Corvinus University of Budapest, Budapest.

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Abstract

In this paper, we propose a new long-step interior point method for solving sufficient linear complementarity problems. The new algorithm combines two important approaches from the literature: the main ideas of the long-step interior point algorithm introduced by Ai and Zhang, and the algebraic equivalent transformation technique proposed by Darvay. Similarly to the method of Ai and Zhang, our algorithm also works in a wide neighbourhood of the central path and has the best known iteration complexity of short-step variants. We implemented the new method in Matlab and tested its efficiency on both sufficient and non-sufficient problem instances. In addition to presenting our numerical results, we also make some interesting observations regarding the analysis of Ai-Zhang type methods.

Item Type:Monograph (Working Paper)
Series Name:Corvinus Economics Working Papers - CEWP
Series Number / Identification Number:2022/03
Uncontrolled Keywords:Mathematical programming, Linear complementarity optimization, Interior point algorithms, Algebraic equivalent transformation technique, sufficient matrices
JEL classification:C61 - Optimization Techniques; Programming Models; Dynamic Analysis
Divisions:Faculty of Economics > Department of Operations Research and Actuarial Sciences
Subjects:Mathematics, Econometrics
Funders:NRDI Fund
Projects:TKP2020 NC
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ID Code:7233
Deposited By: Ádám Hoffmann
Deposited On:07 Mar 2022 15:06
Last Modified:07 Mar 2022 15:06

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