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• W2058361915 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 schemes and Gauss- Seidel (i.e., sequential) ones, as well as virtually all possibilities in between with only a subset of variables updated at each iteration. Our theoretical convergence results improve on existing ones, and numerical results on LASSO, logistic regression, and some nonconvex quadratic problems show that the new method consistently outperforms existing algorithms." @default.
• W2058361915 created "2016-06-24" @default.
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• W2058361915 date "2015-04-01" @default.
• W2058361915 modified "2023-10-01" @default.
• W2058361915 title "Parallel Selective Algorithms for Nonconvex Big Data Optimization" @default.
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• W2058361915 doi "https://doi.org/10.1109/tsp.2015.2399858" @default.
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