The limiting factors of second-order methods for large-scale semidefinite optimization are
the storage and factorization of the Newton matrix. For a particular algorithm based on the …

The search directions in an interior-point method for large scale semidefinite programming
(SDP) can be computed by applying a Krylov iterative method to either the Schur …

The SDPA (SemidDefinite Programming Algorithm) is known as efficient computer software
based on the primal–dual interior-point method for solving SDPs (SemiDefinite Programs) …

This paper studies an inexact perturbed path-following algorithm in the framework of
Lagrangian dual decomposition for solving large-scale separable convex programming …

We introduce a convex optimization modeling framework that transforms a convex
optimization problem expressed in a form natural and convenient for the user into an …

Conic programming, especially semidefinite programming (SDP), has been regarded as
linear programming for the 21st century. This tremendous excitement was spurred in part by …

This paper offers a matrix-free first-order numerical method to solve large-scale conic
optimization problems. Solving systems of linear equations pose the most computationally …

We introduce a convex optimization modeling framework that transforms a convex
optimization problem expressed in a form natural and convenient for the user into an …

We present a collection of computer programs for the solution of linear and nonlinear
semidefinite optimization problems. After briefly discussing the underlying algorithm, the …

The thesis investigates linear programming approaches to solving Semidefinite
Programming (SDP's). One of the various characterizations of the positive semidefiniteness …