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Convergence of cyclic steepest descent for quadratics with two distinct eigenvalues
Yu Li, Qihang Wang
The cyclic steepest descent method computes the exact steepest descent stepsize at the start of each cycle and reuses it for a fixed number of iterations. We study this met…
Riemannian Momentum Tracking: Distributed Optimization with Momentum on Compact Submanifolds
Jun Chen, Tianyi Zhu, Haishan Ye +5
Gradient descent with momentum has been widely applied in various signal processing and machine learning tasks, demonstrating a notable empirical advantage over standard gradient d…
A Unified Zeroth-Order Optimization Framework via Oblivious Randomized Sketching
Haishan Ye, Xiangyu Chang, Xi Chen
We propose a new framework for analyzing zeroth-order optimization (ZOO) from the perspective of \emph{oblivious randomized sketching}.In this framework, commonly used gradient est…
Can a One-Point Feedback Zeroth-order Algorithm Achieve Linear Dimension Dependent Sample Complexity?
Haishan Ye, Xiangyu Chang
We revisit the one-point feedback zeroth-order (ZO) optimization problem, a classical setting in derivative-free optimization where only a single noisy function evaluation is avail…
Anderson Acceleration Without Restart: A Novel Method with -Step Super Quadratic Convergence Rate
Haishan Ye, Dachao Lin, Xiangyu Chang +1
In this paper, we propose a novel Anderson's acceleration method to solve nonlinear equations, which does \emph{not} require a restart strategy to achieve numerical stability. We p…