Optimal Scaling of a Gradient Method for Distributed Resource Allocation

Lin Xiao and Stephen Boyd

June 2006

We consider a class of weighted gradient methods for distributed resource allocation over a network. Each node of the network is associated with a local variable and a convex cost function; the sum of the variables (resources) across the network is fixed. Starting with a feasible allocation, each node updates its local variable in proportion to the differences between the marginal costs of itself and its neighbors. We focus on how to choose the proportional weights on the edges (scaling factors for the gradient method) to make this distributed algorithm converge, and how to make the convergence as fast as possible. We give sufficient conditions on the edge weights for the algorithm to converge monotonically to the optimal solution; these conditions have the form of a linear matrix inequality. We give some simple, explicit methods to choose the weights that satisfy these sufficient conditions. We also derive a guaranteed convergence rate for the algorithm, and find the weights that minimize this rate by solving a semidefinite program. Finally, we extend the main results to problems with general equality constraints, and problems with block separable objective function.

Publication type | Article |

Published in | Journal of Optimization Theory and Applications (JOTA) |

URL | http://stanford.edu/~boyd/papers/fast_redstb.html |

Pages | 469-488 |

Volume | 129 |

Number | 3 |

Publisher | Springer-Verlag All copyrights reserved by Springer 2007. |

- An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization
- Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss
- Distributed Average Consensus with Least-Mean-Square Deviation

> Publications > Optimal Scaling of a Gradient Method for Distributed Resource Allocation