Corvinus
Corvinus

Interior-point algorithms for symmetric cone horizontal linear complementarity problems based on a new class of algebraically equivalent transformations

Darvay, Zsolt and Rigó, Petra Renáta (2022) Interior-point algorithms for symmetric cone horizontal linear complementarity problems based on a new class of algebraically equivalent transformations. Working Paper. Corvinus University of Budapest, Budapest.

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
361kB

Abstract

We introduce interior-point algorithms (IPAs) for solving P_* (κ)-horizontal linear complementarity problems over Cartesian product of symmetric cones. We generalize the primal-dual IPAs proposed recently by Illés et al. [21] to P_* (κ)-horizontal linear complementarity problems over Cartesian product of symmetric cones. In the algebraic equivalent transformation (AET) technique we use a modification of the class of AET functions proposed by Illés et al. [21]. In the literature, there are only few classes of functions for determination of search directions. The class of AET functions used in this paper differs from the other classes appeared in the literature. We prove that the proposed IPAs have the same complexity bound as the best known interior-point methods for solving these types of problems.

Item Type:Monograph (Working Paper)
Series Name:Corvinus Economics Working Papers - CEWP
Series Number / Identification Number:2022/04
Uncontrolled Keywords:Horizontal linear complementarity problem; Cartesian product of symmetric cones; new class of AET functions; interior-point algorithms
JEL classification:C61 - Optimization Techniques; Programming Models; Dynamic Analysis
Divisions:Faculty of Economics > Department of Operations Research and Actuarial Sciences
Subjects:Mathematics, Econometrics
References:
ID Code:7456
Deposited By: Ádám Hoffmann
Deposited On:20 Jun 2022 14:58
Last Modified:20 Jun 2022 14:58

Repository Staff Only: item control page

Downloads

Downloads per month over past year

View more statistics