Abstract
Fast gradient methods (FGM) are very popular in the field of large scale convex optimization problems. Recently, it has been shown that restart strategies can guarantee global linear convergence for non-strongly convex optimization problems if a quadratic functional growth condition is satisfied [1], [2]. In this context, a novel restart FGM algorithm with global linear convergence is proposed in this paper. The main advantages of the algorithm with respect to other linearly convergent restart FGM algorithms are its simplicity and that it does not require prior knowledge of the optimal value of the objective function or of the quadratic functional growth parameter. We present some numerical simulations that illustrate the performance of the algorithm.
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