Sophie Jan-Jégou – Optimization Online

A BFGS-IP algorithm for solving strongly convex optimization problems with feasibility enforced by an exact penalty approach

Published: 2001/02/01

Constrained Nonlinear Optimization analytic center, bfgs quasi-newton approximations, constrained optimization, convex optimization, infeasible iterates, interior point methods, line-search, primal-dual algorithms, shift and slack variables, superlinear convergence

This paper introduces and analyses a new algorithm for minimizing a convex function subject to a finite number of convex inequality constraints. It is assumed that the Lagrangian of the problem is strongly convex. The algorithm combines interior point methods for dealing with the inequality constraints and quasi-Newton techniques for accelerating the convergence. Feasibility of … Read more