TY - JOUR
AU - Weifeng Wang
AU - Qiuyu Wang
PY - 2014/02/22
Y2 - 2021/10/19
TI - Approximated Function Based Spectral Gradient Algorithm for Sparse Signal Recovery
JF - Statistics, Optimization & Information Computing
JA - Stat., optim. inf. comput.
VL - 2
IS - 1
SE - Research Articles
DO - 10.19139/soic.v2i1.33
UR - http://www.iapress.org/index.php/soic/article/view/20140302
AB - Numerical algorithms for the l0-norm regularized non-smooth non-convex minimization problems have recently became a topic of great interest within signal processing, compressive sensing, statistics, and machine learning. Nevertheless, the l0-norm makes the problem combinatorial and generally computationally intractable. In this paper, we construct a new surrogate function to approximate l0-norm regularization, and subsequently make the discrete optimization problem continuous and smooth. Then we use the well-known spectral gradient algorithm to solve the resulting smooth optimization problem. Experiments are provided which illustrate this method is very promising.
ER -