A block coordinate descent method for sensor network localization

A half-quadratic block-coordinate descent method for spectral estimation

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Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization

We study the convergence properties of a (block) coordinate descent method applied to minimize a nondifferentiable (nonconvex) function f (x1 , . . . , xN ) with certain separability and regularity properties. Assuming that f is continuous on a compact level set, the subsequence convergence of the iterates to a stationary point is shown when either f is pseudoconvex in every pair of coordinate ...

Robust Block Coordinate Descent

In this paper we present a novel randomized block coordinate descent method for the minimization of a convex composite objective function. The method uses (approximate) partial second-order (curvature) information, so that the algorithm performance is more robust when applied to highly nonseparable or ill conditioned problems. We call the method Robust Coordinate Descent (RCD). At each iteratio...

Block coordinate descent methods for semidefinite programming

Block Coordinate Descent for Sparse NMF

Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data analysis. An important variant is the sparse NMF problem which arises when we explicitly require the learnt features to be sparse. A natural measure of sparsity is the L0 norm, however its optimization is NP-hard. Mixed norms, such as L1/L2 measure, have been shown to model sparsity robustly, based on intuitive attribu...