Solving large scale convex semidefinite programming (SDP) problems has long been a challenging task numerically. Fortunately, several powerful solvers including SDPNAL, SDPNAL+ and QSDPNAL have recently been developed to solve linear and convex quadratic SDP problems to high accuracy successfully. These solvers are based on the augmented Lagrangian method (ALM) applied to the dual problems with...

The convergence of primal and dual central paths associated to entropy and exponential functions, respectively, for semidefinite programming problem are studied in this paper. As an application, the proximal point method with the Kullback-Leibler distance applied to semidefinite programming problems is considered, and the convergence of primal and dual sequences is proved.

We propose a path-following version of the Todd-Burrell procedure to solve linear programming problems with an unknown optimal value. The path-following scheme is not restricted to Karmarkar's primal step; it can also be implemented with a dual Newton step or with a primal-dual step.

Quantified Boolean Formulas (QBF) provide a good language for modeling many complex questions about deterministic systems, especially questions involving control of such systems and optimizing choices. However, translators typically have one set way to encode the description of a system and a property, or question about the system, often without distinguishing between the two. In many cases the...

We present primal-dual interior-point algorithms with polynomial iteration bounds to nd approximate solutions of semidenite programming problems. Our algorithms achieve the current best iteration bounds and, in every iteration of our algorithms, primal and dual objective values are strictly improved.

About 15 years ago, Goemans and Williamson formally introduced the primal-dual framework for approximation algorithms and applied it to a class of network design optimization problems. Since then literally hundreds of results appeared that extended, modified and applied the technique to a wide range of optimization problems. In this paper we define a class of cost-sharing games arising from Goe...

We consider a primal-dual short-step interior-point method for conic convex optimization problems for which exact evaluation of the gradient and Hessian of the primal and dual barrier functions is either impossible or prohibitively expensive. As our main contribution, we show that if approximate gradients and Hessians of the primal barrier function can be computed, and the relative errors in su...

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 ...

An efficient and numerically stable dual algorithm for positive definite quadratic programming is described which takes advantage of the fact lhat the unconstrained minimum of the objective function can be used as a starling point. Its implementation utilizes the Cholesky and QR factorizations and procedures for updating them. The performance of the dual algorithm is compared against that of pr...

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...