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- W2963596156 abstract "The problem of minimizing a sum of local convex objective functions over a networked system captures many important applications and has received much attention in the distributed optimization field. Most of existing work focuses on development of fast distributed algorithms under the presence of a central clock. The only known algorithms with convergence guarantees for this problem in asynchronous setup could achieve either sublinear rate under totally asynchronous setting or linear rate under partially asynchronous setting (with bounded delay). In this work, we built upon existing literature to develop and analyze an asynchronous Newton based approach for solving a penalized version of the problem. We show that this algorithm converges almost surely with global linear rate and local superlinear rate in expectation. Numerical studies confirm superior performance against other existing asynchronous methods." @default.
- W2963596156 created "2019-07-30" @default.
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- W2963596156 date "2017-12-01" @default.
- W2963596156 modified "2023-09-27" @default.
- W2963596156 title "Superlinearly convergent asynchronous distributed network newton method" @default.
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- W2963596156 doi "https://doi.org/10.1109/cdc.2017.8264076" @default.
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