Low-Complexity RLS Algorithms Using Dichotomous Coordinate Descent Iterations

Low-complexity widely linear RLS filter using DCD iterations

Resumo— Recentemente, filtros adaptativos amplamente lineares estão sendo usados para acessar completamente estatı́sticas de segunda ordem de sinais impróprios, com o objetivo de melhorar a estimação. Essa caracterı́stica torna esses filtros vantajosos em relação aos seus equivalentes estritamente lineares, apesar de apresentarem maior complexidade computacional. Nesse sentido, com o intuito de r...

New multichannel modified filtered-x algorithms for active noise control using the dichotomous coordinate descent method

Coordinate descent algorithms

Coordinate descent algorithms solve optimization problems by successively performing approximate minimization along coordinate directions or coordinate hyperplanes. They have been used in applications for many years, and their popularity continues to grow because of their usefulness in data analysis, machine learning, and other areas of current interest. This paper describes the fundamentals of...

Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity

We study the problem of minimizing the sum of a smooth convex function and a convex blockseparable regularizer and propose a new randomized coordinate descent method, which we call ALPHA. Our method at every iteration updates a random subset of coordinates, following an arbitrary distribution. No coordinate descent methods capable to handle an arbitrary sampling have been studied in the literat...

Inexact Coordinate Descent: Complexity and Preconditioning

In this paper we consider the problem of minimizing a convex function using a randomized block coordinate descent method. One of the key steps at each iteration of the algorithm is determining the update to a block of variables. Existing algorithms assume that in order to compute the update, a particular subproblem is solved exactly. In his work we relax this requirement, and allow for the subp...