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- W4308390411 abstract "Saddle-point problems appear in various settings including machine learning, zero-sum stochastic games, and regression problems. We consider decomposable saddle-point problems and study an extension of the alternating direction method of multipliers to such saddle-point problems. Instead of solving the original saddle-point problem directly, this algorithm solves smaller saddle-point problems by exploiting the decomposable structure. We show the convergence of this algorithm for convex-concave saddle-point problems under a mild assumption. We also provide a sufficient condition for which the assumption holds. We demonstrate the convergence properties of the saddle-point alternating direction method of multipliers with numerical examples on a power allocation problem in communication channels and a network routing problem with adversarial costs." @default.
- W4308390411 created "2022-11-11" @default.
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- W4308390411 date "2022-09-27" @default.
- W4308390411 modified "2023-09-27" @default.
- W4308390411 title "Alternating Direction Method of Multipliers for Decomposable Saddle-Point Problems" @default.
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- W4308390411 doi "https://doi.org/10.1109/allerton49937.2022.9929349" @default.
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