The frequency estimation of multiple complex sinusoids in the presence noise is important for many signal processing applications. As already discussed literature, this problem can be reformulated as a sparse representation problem. In letter, such formulation derived and an algorithm based on cyclic coordinate descent (SCCD) estimating parameters proposed. adaptively reduces size used grid, wh...

Sparse learning based feature selection has been widely investigated in recent years. In this study, we focus on the l2,0-norm selection, which is effective for exact top-k but challenging to optimize. To solve general constrained problems, novelly develop a parameter-free optimization framework coordinate descend (CD) method, termed CD-LSR. Specifically, devise skillful conversion from origina...

Abstract The problem of sensor network localization (SNL) can be formulated as a semidefinite programming with rank constraint. We propose new method for solving such SNL problems. factorize matrix the constraint into product two matrices via Burer–Monteiro factorization. Then, we add difference matrices, penalty parameter, to objective function, thereby reformulating an unconstrained multiconv...

We present a stochastic first-order optimization algorithm, named block-cyclic coordinate descent (BCSC), that adds cyclic constraint to block-coordinate in the selection of both data and parameters. It uses different subsets update parameters, thus limiting detrimental effect outliers training set. Empirical tests image classification benchmark datasets show BCSC outperforms state-of-the-art m...

Recently, it was posited that disparate optimization algorithms may be coalesced in terms of a central source emanating from optimal control theory. Here we further this proposition by showing how coordinate descent derived emerging new principle. In particular, show basic can using maximum principle and collection max functions as “control” Lyapunov functions. The convergence the resulting is ...