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Parallel Selective Algorithms for Nonconvex Big Data Optimization

IEEE Transactions on Signal Processing, no. 7 (2015): 1874-1889

Cited: 168|Views37
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Abstract

We propose a decomposition framework for the parallel optimization of the sum of a differentiable (possibly nonconvex) function and a (block) separable nonsmooth, convex one. The latter term is usually employed to enforce structure in the solution, typically sparsity. Our framework is very flexible and includes both fully parallel Jacobi ...More

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