A new analysis of L-SVRP achieves O((nδ/µ + n) log 1/ε) iteration complexity under Hessian similarity and strong convexity, improving on prior O(δ²/µ² + n) bounds in the high-dissimilarity regime.
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Speeding up Stochastic Proximal Optimization in the High Hessian Dissimilarity Setting
A new analysis of L-SVRP achieves O((nδ/µ + n) log 1/ε) iteration complexity under Hessian similarity and strong convexity, improving on prior O(δ²/µ² + n) bounds in the high-dissimilarity regime.