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Fully Distributed Algorithms for Convex Optimization Problems

  • Damon Mosk-Aoyama
  • Tim Roughgarden
  • Devavrat Shah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4731)

Abstract

We describe a distributed algorithm for convex constrained optimization in networks without any consistent naming infrastructure. The algorithm produces an approximately feasible and near-optimal solution in time polynomial in the network size, the inverse of the permitted error, and a measure of curvature variation in the dual optimization problem. It blends, in a novel way, gossip-based information spreading, iterative gradient ascent, and the barrier method from the design of interior-point algorithms.

Keywords

Dual Problem Primal Problem Convex Optimization Problem Error Parameter Dual Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Damon Mosk-Aoyama
    • 1
  • Tim Roughgarden
    • 1
  • Devavrat Shah
    • 2
  1. 1.Department of Computer Science, Stanford University 
  2. 2.Department of Electrical Engineering and Computer Science, MIT 

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