Projected Splitting Methods for Vertical Linear Complementarity Problems

Francesco Mezzadri () and Emanuele Galligani ()
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Francesco Mezzadri: University of Modena and Reggio Emilia
Emanuele Galligani: University of Modena and Reggio Emilia

Journal of Optimization Theory and Applications, 2022, vol. 193, issue 1, No 26, 598-620

Abstract: Abstract In this paper, we generalize the projected Jacobi and the projected Gauss–Seidel methods to vertical linear complementarity problems (VLCPs) characterized by matrices with positive diagonal entries. First, we formulate the methods and show that the subproblems that must be solved at each iteration have an explicit solution, which is easy to compute. Then, we prove the convergence of the proposed procedures when the matrices of the problem satisfy some assumptions of strict or irreducible diagonal dominance. In this context, for simplicity, we first analyze the convergence in the special case of VLCPs of dimension $$2n\times n$$ 2 n × n , and we then generalize the results to VLCPs of an arbitrary dimension $$\ell n\times n$$ ℓ n × n . Finally, we provide several numerical experiments (involving both full and sparse matrices) that show the effectiveness of the proposed approaches. In this context, our methods are compared with existing solution methods for VLCPs. A parallel implementation of the projected Jacobi method in CUDA is also presented and analyzed.

Keywords: Vertical linear complementarity problems; Projected splitting methods; Parallel computing; 65K05; 65H10; 90C33 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10957-021-01922-y

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