In this paper we present a primal-dual interior-point algorithm to solve a class of multi-objective network flow problems. More precisely, our algorithm is an extension of the single-objective primal infeasible dual feasible inexact interior point method for multi-objective linear network flow problems. Our algorithm is contrasted with standard interior point methods and experimental results on...

The computation of the analytic center of the solution set can be important in linear programming applications where it is desirable to obtain a solution that is not near the relative boundary of the solution set. In this work we discuss the eeective computation of the analytic center solution by the use of primal-dual interior-point methods. A primal-dual interior-point algorithm designed for ...

The inexact primal-dual interior point method which is discussed in this paper chooses a new iterate along an approximation to the Newton direction. The method is the Kojima, Megiddo, and Mizuno globally convergent infeasible interior point algorithm The inexact variation is shown to have the same convergence properties accepting a residual in both the primal and dual Newton step equation also ...

This paper proposes a hybrid computational method (DIPS method) which works as a simplex method for solving a standard form linear program, and, simultaneously, as an interior point method for solving its dual. The DIPS method generates a sequence of primal basic feasible solutions and a sequence of dual interior feasible solutions interdependently. Along the sequences, the duality gap decrease...

In this paper we propose an efficient distributed algorithm for solving loosely coupled convex optimization problems. The algorithm is based on a primal-dual interior-point method in which we use the alternating direction method of multipliers (ADMM) to compute the primal-dual directions at each iteration of the method. This enables us to join the exceptional convergence properties of primal-du...

Our original results refer to dualistic structure on primaldual interior-point methods for symmetric cone programs with linear constraints. It is shown that scalings by the Nesterov-Todd direction are generated by middle points of geodesics joining with primal interior points and dual interior points. Finally we relate power classes of search directions with geodesics and weighted geometric mea...

We present a framework for designing and analyzing primal-dual interior-point methods for convex optimization. We assume that a self-concordant barrier for the convex domain of interest and the Legendre transformation of the barrier are both available to us. We directly apply the theory and techniques of interior-point methods to the given good formulation of the problem (as is, without a conic...

In a recent work [3] the authors improved one of the most efficient interior-point approaches for some classes of block-angular problems. This was achieved by adding a quadratic regularization to the logarithmic barrier. This regularized barrier was shown to be self-concordant, thus fitting the general structural optimization interior-point framework. In practice, however, most codes implement ...

Interior point methods proved to be efficient and robust tools for solving large–scale optimization problems. The standard infeasible–start implementations scope very well with wide variety of problem classes, their only serious drawback is that they detect primal or dual infeasibility by divergence and not by convergence. As an alternative, approaches based on skew–symmetric and self–dual refo...

Interior point methods were widely used in the past in the form of barrier methods. In linear programming, the simplex method dominated, mainly due to inefficiencies of barrier methods. Interior point methods became quit popular again after 1984, when Karmarkar announced a fast polynomial-time interior method for nonlinear programming [Karmarkar, 1984]. In this section we present primal-dual in...