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math.NA2026

Quantile Randomized Kaczmarz for Streaming Linear Systems with Massart Noise

Emeric Battaglia, Jian-Feng Cai, Junren Chen +3

Quantile randomized Kaczmarz (QRK) has proven to be an efficient solver for corrupted linear systems and has received much attention. It was recently shown by Cai et al. (SIAM J. M…

math.NA2026

RPLSS: A randomized projected linear systems solver

Meng-Long Xiao, Tao Li, Deanna Needell

The projected linear system solver (PLSS), by incrementally appending columns to a random or deterministic sketching matrix, provides an attractive finite termination property for…

math.NA2026

Attention Mechanisms Through the Lens of Numerical Methods: Approximation Methods and Alternative Formulations

Michel Fabrice Serret, Alice Cortinovis, Yijun Dong +10

The attention mechanism is the computational core of modern Transformer architectures, but its quadratic complexity in the input sequence length is the bottleneck for large-scale i…

math.NA2025

Stochastic Gradient Descent for Incomplete Tensor Linear Systems

Anna Ma, Deanna Needell, Alexander Xue

Solving large tensor linear systems poses significant challenges due to the high volume of data stored, and it only becomes more challenging when some of the data is missing. Recen…

math.NA2025

A federated Kaczmarz algorithm

Halyun Jeong, Deanna Needell, Chi-Hao Wu

In this paper, we propose a federated algorithm for solving large linear systems that is inspired by the classic randomized Kaczmarz algorithm. We provide convergence guarantees of…

math.NA20251 cited

Randomized Kaczmarz Methods with Beyond-Krylov Convergence

Michał Dereziński, Deanna Needell, Elizaveta Rebrova +1

Randomized Kaczmarz methods form a family of linear system solvers which converge by repeatedly projecting their iterates onto randomly sampled equations. While effective in some c…