Abstract
In this paper, we propose a modified proximal gradient method for a class of sparse optimization problems, which arise in many contemporary statistical and signal processing applications. The new method uses a new scheme to construct the descent direction based on the proximal gradient method. It is proven that the modified proximal gradient method is Q-linearly convergent without the assumption of the strong convexity of the objective function. Numerical experiments have been conducted to evaluate the proposed method.
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