Interior-point methods

Interior-point methods (also referred to as barrier methods or IPMs) are a certain class of algorithms that solve linear and nonlinear convex optimization problems. It enabled solutions of linear programming problems that were beyond the capabilities of the simplex method. Contrary to the simplex method, it reaches the best solution by traversing the interior of the feasible region. The method can be generalized to convex programming based on a self-concordant barrier function used to encode the convex set. The class of primal-dual path-following interior-point methods is considered the most successful. Mehrotra’s predictor-corrector algorithm provides the basis for most implementations of this class of methods.

Co-sponsored by: Control and Signal Processing Research Group

Speaker(s): Mr. Zohaib Latif,

Capital University of Science & Technology (CUST), Islamabad, Islamabad Capital Territory, Pakistan