In this paper, we propose a path-following interior-point method (IPM) for solving linear
optimization (LO) problems based on a new kernel function (KF). The latter differs from other …

We present a feasible kernel-based interior point method (IPM) to solve a monotone linear
complementarity problem (LCP) which is based on an eligible kernel function with new …

In this paper, new search directions and proximity measures are proposed for P∗(κ)-linear
complementarity problem. The new method is based on a new class of kernel function which …

In this paper, we first present a polynomial-time primal-dual interior-point method (IPM) for
solving linear programming (LP) problems, based on a new kernel function (KF) with a …

This thesis topic falls within the realm of linear optimization and semidefinite optimization.
The objective is to study primal-dual interior-point methods for solving linear optimization …

We present an interior-point algorithmic framework for P_*(κ)-Linear Complementarity
Problems that is based on a barrier function which is defined by a new class of univariate …

In this paper, we present a primal-dual interior point algorithm for semidefinite optimization
problems based on a new class of kernel functions. These functions constitute a …

The purpose of this paper is to obtain new complexity results for solving the semidefinite
optimization (SDO) problem. We define a new proximity function for the SDO by a new …

In this paper, we present the implementation of infeasible interior-point methods (IIPMs) for
linear and nonlinear optimization with the full-Newton step based on an algebraic equivalent …

Dans cette thèse, une classe de méthodes de points intérieurs primales-duales (MPIs) pour
résoudre des problèmes de programmation quadratique convexe est présentée. C'est une …