1 citations · 1 across the 5 of their papers we have counts for
5 papers
Adam Improves Muon: Adaptive Moment Estimation with Orthogonalized Momentum
Minxin Zhang, Yuxuan Liu, Hayden Schaeffer
Efficient stochastic optimization typically integrates an update direction that performs well in the deterministic regime with a mechanism adapting to stochastic perturbations. Whi…
AdaGrad Meets Muon: Adaptive Stepsizes for Orthogonal Updates
Minxin Zhang, Yuxuan Liu, Hayden Schaeffer
The recently proposed Muon optimizer updates weight matrices via orthogonalized momentum and has demonstrated strong empirical success in large language model training. However, it…
Inexact Proximal Point Algorithms for Zeroth-Order Global Optimization
Minxin Zhang, Fuqun Han, Yat Tin Chow +2
This work concerns the zeroth-order global minimization of continuous nonconvex functions with a unique global minimizer and possibly multiple local minimizers. We formulate a theo…
Block Matrix and Tensor Randomized Kaczmarz Methods for Linear Feasibility Problems
Minxin Zhang, Jamie Haddock, Deanna Needell
The randomized Kaczmarz methods are a popular and effective family of iterative methods for solving large-scale linear systems of equations, which have also been applied to linear…
Projected-Search Methods for Bound-Constrained Optimization
Michael W. Ferry, Philip E. Gill, Elizabeth Wong +1
Projected-search methods for bound-constrained optimization are based on performing a search along a piecewise-linear continuous path obtained by projecting a search direction onto…