Krylov subspace acceleration for first-order methods on convex QPs outperforms Anderson acceleration in iterations and often runtime by avoiding ill-conditioning during slow convergence.
Rate of Convergence Analysis of Decom- position Methods Based on the Proximal Method of Multipliers for Convex Minimization,
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Krylov Subspace Acceleration for First-Order Splitting Methods in Convex Quadratic Programming
Krylov subspace acceleration for first-order methods on convex QPs outperforms Anderson acceleration in iterations and often runtime by avoiding ill-conditioning during slow convergence.