01-08-2013 дата публикации
Номер: US20130198372A1
A distributed inexact Newton-type second order method for Network Utility maximization problems is provided. Such methods are capable of achieving superlinear convergence rates (in primal iterates) to some error neighborhood, can be implemented in a decentralized manner using a matrix splitting scheme, and is compatible with current information exchange mechanisms. 1. A network utility maximization method , executable on one or more processors , comprising:(a) determining, using the one or more processors, a price for one or more links;(b) repeating step (a), using the one or more processors, based on an error parameter and an aggregated weighed price;(c) determining, using the one or more processors, a stepsize parameter based on a diagonal matrix and weighted prices;(d) determining, using the one or more processors, a slack variable based on the aggregated weighted price;(e) determining, using the one or more processors, a primal vector based on the aggregated weighted price, the stepsize parameter, and the slack variable;(f) repeating, using the one or more processors, steps (a) through (e) based on a threshold amount and the primal vector.2. The method of claim 1 , wherein the primal vector is indicative of an optimal source rates for a destination computing device and a source computing device.3. The method of claim 1 , wherein a destination computing device processes steps (a) claim 1 , (b) claim 1 , (c) claim 1 , (d) claim 1 , (e) claim 1 , and (f) for source rates.4. The method of claim 1 , wherein a plurality of destination computing devices process steps (a) claim 1 , (b) claim 1 , (c) claim 1 , (d) claim 1 , (e) claim 1 , and (f) for each respective source rate associated with the respective destination computing device.5. The method of claim 1 , wherein the primal vector is determined utilizing a second-order function.6. The method of claim 1 , wherein step (a) further comprises:(a-1) separating a Hessian related matrix into a plurality of parts; and(a-2 ...
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