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math.OC2024
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates
Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
We consider the finite-sum optimization problem, where each component function is strongly convex and has Lipschitz continuous gradient and Hessian. The recently proposed increment…
math.OC2023
Communication Efficient Distributed Newton Method with Fast Convergence Rates
Chengchang Liu, Lesi Chen, Luo Luo +1
We propose a communication and computation efficient second-order method for distributed optimization. For each iteration, our method only requires communication c…