We develop a modulus-based framework to solve weighted horizontal linear complementarity problems (WHLCPs). First, we reformulate the WHLCP as a modulus-based system whose solution, in general, is not unique. We characterize the solutions by discussing their sign pattern and how they are linked to one another. After this analysis, we exploit the modulus-based formulation to develop new solution methods. In particular, we present a non-smooth Newton iteration and a matrix splitting method for solving WHLCPs. We prove the local convergence of both methods under some assumptions. Finally, we solve numerical experiments involving symmetric and non-symmetric matrices. In this context, we compare our approaches with a recently proposed smoothing Newton's method. The experiments include problems taken from the literature. We also provide numerical insights on relevant parts of the algorithms, such as convergence, attraction basin, and starting iterate.
A modulus-based framework for weighted horizontal linear complementarity problems / Mezzadri, F.. - In: APPLIED MATHEMATICS AND COMPUTATION. - ISSN 0096-3003. - 495:(2025), pp. 1-19. [10.1016/j.amc.2025.129313]
A modulus-based framework for weighted horizontal linear complementarity problems
Mezzadri F.
2025
Abstract
We develop a modulus-based framework to solve weighted horizontal linear complementarity problems (WHLCPs). First, we reformulate the WHLCP as a modulus-based system whose solution, in general, is not unique. We characterize the solutions by discussing their sign pattern and how they are linked to one another. After this analysis, we exploit the modulus-based formulation to develop new solution methods. In particular, we present a non-smooth Newton iteration and a matrix splitting method for solving WHLCPs. We prove the local convergence of both methods under some assumptions. Finally, we solve numerical experiments involving symmetric and non-symmetric matrices. In this context, we compare our approaches with a recently proposed smoothing Newton's method. The experiments include problems taken from the literature. We also provide numerical insights on relevant parts of the algorithms, such as convergence, attraction basin, and starting iterate.File | Dimensione | Formato | |
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