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.
Beck, First-order methods in optimization
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A variable smoothing method for DC composite optimization is proposed for robust phase retrieval, with convergence to DC critical points and experiments indicating better outlier robustness than ℓ1 loss.
Proposes FP-type receiver unifying projection and SIC approaches via tradeoff factor for uplink ISAC joint detection and estimation, derives PEP for ML/ZF detectors, and extends to dynamic DFP receiver via homotopy optimization.
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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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A DC Composite Optimization via Variable Smoothing for Robust Phase Retrieval with Nonconvex Loss Functions
A variable smoothing method for DC composite optimization is proposed for robust phase retrieval, with convergence to DC critical points and experiments indicating better outlier robustness than ℓ1 loss.
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A Framework for Uplink ISAC Receiver Designs: Performance Analysis and Algorithm Development
Proposes FP-type receiver unifying projection and SIC approaches via tradeoff factor for uplink ISAC joint detection and estimation, derives PEP for ML/ZF detectors, and extends to dynamic DFP receiver via homotopy optimization.