Convergence of a Newton-like algorithm solving the nonlinear complementarity problem Online publication date: Sat, 09-May-2015
by Vyacheslav V. Kalashnikov; Nataliya I. Kalashnykova
International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP), Vol. 4, No. 4, 2014
Abstract: In the paper, we examine conditions that guarantee the existence of a solution to the parametric nonlinear complementarity problem with a monotone (with respect to x) mapping f: Rn × Rm → Rn and ensure a Newton-like method convergence if the mapping f is strictly monotone and each of its components fi is a concave function. In addition to that, we prove that the sequence of approximate solutions converges at a quadratic rate.
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