An interior point approach for linear complementarity problem using new parametrized kernel function

Complexity analysis of an interior-point algorithm for linear optimization based on a new parametric kernel function with a double barrier term

“…Introduction. Kernel functions play an important role in defining new search directions for primal-dual interior-point algorithm for solving linear optimization (LO) problems [1,5,3], Linear Complementarity Problems (LCP) [6,24], convex quadratic optimization (CQO) [28], second-order cone optimization (SOCO) [4], semidefinite optimization (SDO) [20,27,13], symmetric optimization (SO) [26], the Cartesian P * (κ)-LCP over symmetric cones [10,15] and is a very active research areas in mathematical programming. In general, each kernel function gives rise to a search direction, and determines a primal-dual interior-point algorithm.…”