The paper proposes Consensus ALADIN (C-ALADIN) algorithms that solve distributed consensus optimization with global convergence for convex problems and local convergence for non-convex ones, including a decentralized version over directed graphs using quantized communication.
Consensus ALADIN: A framework for distrib uted optimization and its application in federated learning,
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Mix-CALADIN is a distributed algorithm for consensus mixed-integer optimization that provides convergence guarantees under Lipschitz continuity for both convex and nonconvex problems.
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Distributed and Decentralized Optimization Algorithms via Consensus ALADIN
The paper proposes Consensus ALADIN (C-ALADIN) algorithms that solve distributed consensus optimization with global convergence for convex problems and local convergence for non-convex ones, including a decentralized version over directed graphs using quantized communication.
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Mix-CALADIN: A Distributed Algorithm for Consensus Mixed-Integer Optimization
Mix-CALADIN is a distributed algorithm for consensus mixed-integer optimization that provides convergence guarantees under Lipschitz continuity for both convex and nonconvex problems.