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The performance of fast forward–backward splitting, or equivalently fast proximal gradient methods, depends on the conditioning of the optimization problem data. This conditioning is related to a metric that is defined by the space on which the optimization problem is stated; selecting a space on which the optimization data is better conditioned improves the performance of the algorithm. In this paper,...
We propose a distributed optimization algorithm for mixed L1/L2-norm optimization based on accelerated gradient methods using dual decomposition. The algorithm achieves convergence rate O(1k2), where k is the iteration number, which significantly improves the convergence rates of existing duality-based distributed optimization algorithms that achieve O(1k). The performance of the developed algorithm...
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