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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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8 results for “Newton-Schulz”

Convergence of Muon with Newton-Schulzstat.ML

Muon with Newton-Schulz converges to the same stationary point as SVD-polar, up to a constant factor.

problem Improving the convergence rate of Muon optimizer.

method Using Newton-Schulz steps for momentum orthogonalization, proving convergence rate and constant factor.

result Muon with Newton-Schulz converges to the same stationary point as SVD-polar, up to a constant factor.

Muon optimizer Newton-Schulz convergence SVD-polar momentum

2026-01-27abs ↗pdf ↗

NS-RGS: Newton-Schulz based Riemannian gradient method for orthogonal group synchronizationstat.ML

NS-RGS improves orthogonal group synchronization with faster convergence.

problem Orthogonal group synchronization from pairwise measurements.

method Newton-Schulz iteration for Riemannian gradient optimization.

result NS-RGS achieves linear convergence and near-optimal accuracy.

group synchronization orthogonal group Newton-Schulz Riemannian gradient GPU/TPU

2026-04-07abs ↗pdf ↗

AuON: A Linear-time Alternative to Orthogonal Momentum Updatescs.LG

AuON is a linear-time optimizer that improves upon Muon's performance without approximate orthogonal matrices.

problem High memory and computational costs of orthogonal momentum updates.

method AuON uses normalized nonlinear scaling and a 'emergency brake' to handle exploding attention logits.

result AuON achieves strong performance without approximate orthogonal matrices, preserving structural alignment and reconditioning.

orthogonal momentum linear-time optimizer attention logits Muon Newton-Schulz iterations

2025-09-29abs ↗pdf ↗

Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noisecs.IT

Method recovers complex-valued signals from speckle-noised measurements.

problem Recovering complex-valued signals from speckle-noised measurements.

method Bagged Deep Image Priors integrated with projected gradient descent and Newton-Schulz algorithm.

result Achieves state-of-the-art performance in MSE reduction.

speckle noise complex-valued signal likelihood-based methods maximum likelihood estimator deep image prior

2024-02-23abs ↗pdf ↗

MuonEq: Balancing Before Orthogonalization with Lightweight Equilibrationcs.LG

MuonEq improves training of matrix-valued parameters by rebalancing momentum before orthogonalization.

problem Training matrix-valued parameters with orthogonalized-update optimizers like Muon.

method MuonEq introduces three lightweight pre-orthogonalization equilibration schemes: two-sided row/column normalization (RC), row normalization (R), and column normalization (C).

result Row/column normalization acts as a zeroth-order surrogate for whitening and improves the geometry seen by orthogonalization.

Muon orthogonalization matrix-valued parameters pre-orthogonalization rebalancing

2026-03-30abs ↗pdf ↗

Multilook Coherent Imaging: Theoretical Guarantees and Algorithmsstat.ML

The paper addresses speckle noise in coherent imaging systems.

problem Speckle noise degrades image quality in coherent imaging systems.

method Theoretical and algorithmic analysis of likelihood-based approaches for multilook coherent imaging.

result Established the first theoretical upper bound on MSE of the maximum likelihood estimator.

coherent imaging speckle noise maximum likelihood likelihood-based approaches projected gradient descent

2025-05-29abs ↗pdf ↗

Modular Duality in Deep Learningcs.LG

New theory maps neural network weights to optimize faster and scale.

problem Optimizing neural networks for speed and scalability.

method Constructing a duality map using layer-wise operator norms.

result Derived GPU-friendly algorithms for various layers.

neural networks optimization duality operator norms GPU

2024-10-28abs ↗pdf ↗

A Muon-Accelerated Algorithm for Low Separation Rank Tensor Generalized Linear Modelsstat.ML

Improved tensor GLM estimation for complex data.

problem Complex tensor data in GLMs leads to high-dimensional, ill-posed estimation.

method Proposed LSRTR-M algorithm using Muon updates for faster convergence and lower errors.

result LSRTR-M converges faster and achieves lower errors than LSRTR.

Tensor GLMs Low Separation Rank Muon updates Scalable estimation Computational efficiency

2026-04-06abs ↗pdf ↗