3 citations · 12 across the 27 of their papers we have counts for
4 papers · 1 filter
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…
Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms
Yuchen Li, Laura Balzano, Deanna Needell +1
We analyze inexact Riemannian gradient descent (RGD) where Riemannian gradients and retractions are inexactly (and cheaply) computed. Our focus is on understanding when inexact RGD…
Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization
Yuchen Li, Laura Balzano, Deanna Needell +1
Block majorization-minimization (BMM) is a simple iterative algorithm for nonconvex optimization that sequentially minimizes a majorizing surrogate of the objective function in eac…
Iterative Singular Tube Hard Thresholding Algorithms for Tensor Recovery
Rachel Grotheer, Shuang Li, Anna Ma +2
Due to the explosive growth of large-scale data sets, tensors have been a vital tool to analyze and process high-dimensional data. Different from the matrix case, tensor decomposit…