Random Block Coordinate Descent Methods for Linearly Constrained Optimization over Networks

Random Block Coordinate Descent Methods for Linearly Constrained Optimization over Networks

In this paper we develop random block coordinate descent methods for minimizing large-scale linearly constrained convex problems over networks. Since coupled constraints appear in the problem, we devise an algorithm that updates in parallel at each iteration at least two random components of the solution, chosen according to a given probability distribution. Those computations can be performed ...

Block coordinate descent methods for semidefinite programming

A coordinate gradient descent method for linearly constrained smooth optimization and support vector machines training

Support vector machines (SVMs) training may be posed as a large quadratic program (QP) with bound constraints and a single linear equality constraint. We propose a (block) coordinate gradient descent method for solving this problem and, more generally, linearly constrained smooth optimization. Our method is closely related to decomposition methods currently popular for SVM training. We establis...

Spectral gradient methods for linearly constrained optimization

Linearly constrained optimization problems with simple bounds are considered in the present work. First, a preconditioned spectral gradient method is defined for the case in which no simple bounds are present. This algorithm can be viewed as a quasiNewton method in which the approximate Hessians satisfy a weak secant equation. The spectral choice of steplength is embedded into the Hessian appro...

Sequence Estimation over Linearly-Constrained Random Channels

This paper presents a new approach using EM (Expectation-Maximization) algorithms for ML (maximum likelihood) sequence estimation over unknown ISI (inter-symbol interference) channels with linearly–constrained random channel coefficients which may be fast time-varying. By using the EM formulation to marginalize over the underlying channel coefficient distribution, maximum-likelihood estimates o...