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A Penalty Alternating Direction Method Of Multipliers For Decentralized Composite Optimization
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A Penalty Alternating Direction Method Of Multipliers For Decentralized Composite Optimization
This paper proposes a penalty alternating direction method of multipliers (ADMM) to minimize the summation of convex composite functions over a decentralized network. Each agent in the network holds a private function consisting of a smooth part and a non
This paper proposes a penalty alternating direction method of multipliers (ADMM) to minimize the summation of convex composite functions over a decentralized network. Each agent in the network holds a private function consisting of a smooth part and a non
