In this paper we develop several polynomial-time interior-point methods (IPM) for solving nonlinear primal-dual conic optimization problem. We assume that the barriers for the primal and the dual cone are not conjugate. This broken symmetry does not allow to apply the standard primal-dual IPM. However, we show that in this situation it is also possible to develop very efficient optimization met...

A new primal-dual algorithm is presented for solving a class of non-convex minimization problems. This algorithm is based on canonical duality theory such that the original non-convex minimization problem is first reformulated as a convex-concave saddle point optimization problem, which is then solved by a quadratically perturbed primal-dual method. Numerical examples are illustrated. Comparing...

We consider a network simplex method using the primal-dual symmetric pivoting rule proposed by Chen, Pardalos, and Saunders. For minimum-cost network-flow problems, we prove global convergence of the algorithm and propose a new scheme in which the algorithm can start from an arbitrary pair of primal and dual feasible spanning trees. For shortest-path problems, we prove the strongly polynomial t...

We discuss an adaptation of the famous primal-dual 1matching algorithm to balanced network flows which can be viewed as a network flow description of capacitated matching problems. This method is endowed with a sophisticated start-up procedure which eventually makes the algorithm strongly polynomial. We apply the primal-dual algorithm to the shortest valid path problem with arbitrary arc length...

A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to simple bounds and linear equality constraints. The method alternates between a classical primal-dual step and a Newton-like step in order to ensure descent on a suitable merit function. Convergence of a well-deened subsequence of iterates is proved from arbitrary starting points. Algorithmi...

Last time we discussed how LP-duality can be applied to approximation. We introduced the primaldual method for approximating the optimal solution to an LP in a combinatorial way. We applied this technique to give another 2-approximation for the Vertex Cover problem. In this lecture, we present and analyze a primal-dual algorithm that gives a 2-approximation for the Steiner Forest problem. We al...

Here we present an implementation of Primal-Dual Affine scaling method to solve linear optimisation problem on GPU based systems. Strategies to convert the system generated by complementary slackness theorem into a symmetric system are given. A new CUDA friendly technique to solve the resulting symmetric positive definite subsystem is also developed. Various strategies to reduce the memory tran...

In this paper we consider a linear programming problem with the underlying matrix unimodular, and the other data integer. Given arbitrary near optimum feasible solutions to the primal and the dual problems, we obtain conditions under which statements can be made about the value of certain variables in optimal vertices. Such results have applications to the problem of determining the stopping cr...

In this article, we consider the proximal point method with Bregman distance applied to linear programming problems, and study the dual sequence obtained from the optimal multipliers of the linear constraints of each subproblem. We establish the convergence of this dual sequence, as well as convergence rate results for the primal sequence, for a suitable family of Bregman distances. These resul...

We consider convex optimization and variational inequality problems with a given separable structure. We propose a new decomposition method for these problems which combines the recent logarithmicquadratic proximal theory introduced by the authors with a decomposition method given by Chen-Teboulle for convex problems with particular structure. The resulting method allows to produce for the firs...