FrankWolfe Splitting via Augmented Lagrangian Method
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Proceedings of the TwentyFirst International Conference on Artificial Intelligence and Statistics, PMLR 84:14561465, 2018.
Abstract
Minimizing a function over an intersection of convex sets is an important task in optimization that is often much more challenging than minimizing it over each individual constraint set. While traditional methods such as FrankWolfe (FW) or proximal gradient descent assume access to a linear or quadratic oracle on the intersection, splitting techniques take advantage of the structure of each sets, and only require access to the oracle on the individual constraints. In this work, we develop and analyze the FrankWolfe Augmented Lagrangian (FWAL) algorithm, a method for minimizing a smooth function over convex compact sets related by a “linear consistency” constraint that only requires access to a linear minimization oracle over the individual constraints. It is based on the Augmented Lagrangian Method (ALM), also known as Method of Multipliers, but unlike most existing splitting methods, it only requires access to linear (instead of quadratic) minimization oracles. We use recent advances in the analysis of FrankWolfe and the alternating direction method of multipliers algorithms to prove a sublinear convergence rate for FWAL over general convex compact sets and a linear convergence rate for polytopes.
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