An Interior Point Algorithm for Mixed Complementarity Nonlinear Problems

Angel E. R. Gutierrez, Sandro R. Mazorche, José Herskovits and Grigori Chapiro ()
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Angel E. R. Gutierrez: Instituto de Matemática y Ciencias Afines
Sandro R. Mazorche: Universidade Federal de Juiz de Fora
José Herskovits: Military Institute of Engineering
Grigori Chapiro: Universidade Federal de Juiz de Fora

Journal of Optimization Theory and Applications, 2017, vol. 175, issue 2, No 7, 432-449

Abstract: Abstract Nonlinear complementarity and mixed complementarity problems arise in mathematical models describing several applications in Engineering, Economics and different branches of physics. Previously, robust and efficient feasible directions interior point algorithm was presented for nonlinear complementarity problems. In this paper, it is extended to mixed nonlinear complementarity problems. At each iteration, the algorithm finds a feasible direction with respect to the region defined by the inequality conditions, which is also monotonic descent direction for the potential function. Then, an approximate line search along this direction is performed in order to define the next iteration. Global and asymptotic convergence for the algorithm is investigated. The proposed algorithm is tested on several benchmark problems. The results are in good agreement with the asymptotic analysis. Finally, the algorithm is applied to the elastic–plastic torsion problem encountered in the field of Solid Mechanics.

Keywords: Feasible direction algorithm; Mixed nonlinear complementarity problems; Interior point algorithm; Elastic–plastic torsion; 74C05; 90C33; 90C51 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10957-017-1171-7

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