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• W2053155671 abstract "We present a fast distributed solution to the capacity constrained convex network flow optimization problem. Our solution is based on a distributed approximation of Newtons method called Accelerated Dual Descent (ADD). Our algorithm uses a parameterized approximate inverse Hessian, which is computed using matrix splitting techniques and a Neumann series truncated after N terms. The algorithm is called ADD-N because each update requires information from N-hop neighbors in the network. The parameter N characterizes an explicit trade off between information dependence and convergence rate. Numerical experiments show that even for N=1 and N=2, ADD-N converges orders of magnitude faster than subgradient descent." @default.
• W2053155671 created "2016-06-24" @default.
• W2053155671 creator A5021418299 @default.
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• W2053155671 date "2013-12-01" @default.
• W2053155671 modified "2023-09-26" @default.
• W2053155671 title "Accelerated dual descent for constrained convex network flow optimization" @default.
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• W2053155671 doi "https://doi.org/10.1109/cdc.2013.6760019" @default.
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