Develops a batch-free online covariance estimator for sketched Newton methods, proves its consistency, and demonstrates use for online statistical inference on regression and CUTEst problems.
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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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Online Covariance Matrix Estimation in Sketched Newton Methods
Develops a batch-free online covariance estimator for sketched Newton methods, proves its consistency, and demonstrates use for online statistical inference on regression and CUTEst 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.