Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum

Abstract

This paper applies an idea of adaptive momentum for the nonlinear conjugate gradient to accelerate optimization problems in sparse recovery. Specifically, we consider two types of minimization problems: a (single) differentiable function and the sum of a non-smooth function and a differentiable function. In the first case, we adopt a fixed step size to avoid the traditional line search and establish the convergence analysis of the proposed algorithm for a quadratic problem. This acceleration is further incorporated with an operator splitting technique to deal with the non-smooth function in the second case. We use the convex $$\ell _1$$ and the nonconvex $$\ell _1-\ell _2$$ functionals as two case studies to demonstrate the efficiency of the proposed approaches over traditional methods.