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We introduce the distributed Broyden-Fletcher-Goldfarb-Shanno (D-BFGS) method as an asynchronous decentralized variation of the BFGS quasi-Newton method for solving consensus optimization problems on a penalty function in the primal domain. The D-BFGS method is of interest in problems that are not well conditioned and in which second order information is not readily available, making decentralized...
This paper considers consensus optimization problems where each node of a network has access to a different summand of an aggregate cost function. Nodes try to maximize the aggregate cost function, while they exchange information only with their neighbors. We modify the dual decomposition method to incorporate a curvature correction inspired by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton...
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