Zawadski, Erik Gordon, Geoffrey J. Platzer, Andre A projection algorithm for strictly monotone linear complementarity problems <p>Complementary problems play a central role in equilibrium finding, physical simulation, and optimization. As a consequence, we are interested in understanding how to solve these problems quickly, and this often involves approximation. In this paper we present a method for approximately solving strictly monotone linear complementarity problems with a Galerkin approximation. We also give bounds for the approximate error, and prove novel bounds on perturbation error. These perturbation bounds suggest that a Galerkin approximation may be much less sensitive to noise than the original LCP.</p> Machine Learning 2013-12-01
    https://kilthub.cmu.edu/articles/journal_contribution/A_projection_algorithm_for_strictly_monotone_linear_complementarity_problems/6475421
10.1184/R1/6475421.v1