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

Marianna E. Nagy and Anita Varga
Authors registered in the RePEc Author Service: Marianna Eisenberg-Nagy

Corvinus Economics Working Papers (CEWP) from Corvinus University of Budapest

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.

Keywords: Mathematical programming; Linear complementarity optimization; Interior point algorithms; Algebraic equivalent transformation technique; sufficient matrices (search for similar items in EconPapers)
JEL-codes: C61 (search for similar items in EconPapers)
Date: 2022-03-07
New Economics Papers: this item is included in nep-ban and nep-ore
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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https://unipub.lib.uni-corvinus.hu/7233/ original version (application/pdf)

Related works:
Journal Article: A New Ai–Zhang Type Interior Point Algorithm for Sufficient Linear Complementarity Problems (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:cvh:coecwp:2022/03

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