Singular value soft-thresholding via the polar decomposition
- URL: http://arxiv.org/abs/2607.22484v1
- Date: Fri, 24 Jul 2026 16:55:09 GMT
- Title: Singular value soft-thresholding via the polar decomposition
- Abstract summary: Singular value soft-thresholding can be computed via a reduction to the matrix polar decomposition, which allows one to exploit GPU-friendly algorithms for computing the polar decomposition.<n>There is a significant speed-up on GPUs compared to the standard approach using the SVD.<n>We leave the investigation of robustness to future work, but note that due to the discontinuous nature of the sign function, the reduction to the polar decomposition is likely only suitable for low-accuracy applications.
- Score: 3.2150654072618976
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Singular value soft-thresholding can be computed via a reduction to the matrix polar decomposition, which allows one to exploit GPU-friendly algorithms for computing the polar decomposition. Empirically, there is a significant speed-up on GPUs compared to the standard approach using the SVD. We leave the investigation of robustness to future work, but note that due to the discontinuous nature of the sign function, the reduction to the polar decomposition is likely only suitable for low-accuracy applications.
Related papers
- The Role of Gradient Modification in Heavy-Tailed Nonconvex Stochastic Min-Max Optimization [50.59470683896735]
min-max optimization has attracted increasing attention due to its applications in modern machine learning.<n>We provide a comprehensive theoretical of gradient descent (SGDA)<n>We develop new clipping-free algorithms, i.e., Stoc-GDAM and Stoc-TRmax, and they both can achieve the optimal dependence on the target accuracy without using gradient clipping.
arXiv Detail & Related papers (2026-09-05T12:49:28Z) - Stokes-Informed Diffusion for Robust Linear Polarization Estimation [49.459667092743445]
GenPolar is a Stokes-informed diffusion framework grounded in the Mueller formalism from an intensity observation.<n>It achieves state-of-the-art performance in both DoLP fidelity and AoP stability.<n>These improvements translate to significant and consistent gains in downstream applications, including material detection and de-reflection.
arXiv Detail & Related papers (2026-07-23T11:56:15Z) - Muon with Nesterov Momentum: Heavy-Tailed Noise and (Randomized) Inexact Polar Decomposition [11.258577546422295]
We develop a theory for inexact polar decomposition in non$ matrix optimization undertailed noise.<n>We analyze a randomized low-rank polar decomposition that is substantially more efficient than fullspace methods.
arXiv Detail & Related papers (2026-05-07T19:32:39Z) - The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm [24.020943794160555]
We introduce Polar Express, a new method for computing the polar decomposition.<n>We prove that this strategy minimizes error in a worst-case sense, allowing Polar Express to converge as rapidly as possible.<n>When integrated into the Muon training framework, our method leads to consistent improvements in validation loss.
arXiv Detail & Related papers (2025-05-22T17:23:14Z) - Entrywise error bounds for low-rank approximations of kernel matrices [55.524284152242096]
We derive entrywise error bounds for low-rank approximations of kernel matrices obtained using the truncated eigen-decomposition.
A key technical innovation is a delocalisation result for the eigenvectors of the kernel matrix corresponding to small eigenvalues.
We validate our theory with an empirical study of a collection of synthetic and real-world datasets.
arXiv Detail & Related papers (2024-05-23T12:26:25Z) - An Alternative Graphical Lasso Algorithm for Precision Matrices [0.0]
We present a new/improved (dual-primal) DP-GLasso algorithm for estimating sparse precision matrices.
We show that the regularized normal log-likelihood naturally decouples into a sum of two easy to minimize convex functions one of which is a Lasso regression problem.
Our algorithm has the precision matrix as its optimization target right at the outset, and retains all the favorable properties of the DP-GLasso algorithm.
arXiv Detail & Related papers (2024-03-19T02:01:01Z) - Simple and Fast Polarization Tracking algorithm for Continuous-Variable
Quantum Key Distribution System Using Orthogonal Pilot Tone [15.578774779452209]
Experimental results show that the polarization scrambling rate 12.57 krad/s can be tracked by using the proposed algorithm.
Digital simulations show that about 188.50 Mrad/s can be tracked by the proposed algorithm.
arXiv Detail & Related papers (2023-03-21T02:37:13Z) - Differentiable Annealed Importance Sampling and the Perils of Gradient
Noise [68.44523807580438]
Annealed importance sampling (AIS) and related algorithms are highly effective tools for marginal likelihood estimation.
Differentiability is a desirable property as it would admit the possibility of optimizing marginal likelihood as an objective.
We propose a differentiable algorithm by abandoning Metropolis-Hastings steps, which further unlocks mini-batch computation.
arXiv Detail & Related papers (2021-07-21T17:10:14Z) - Robust Differentiable SVD [117.35644933471401]
Eigendecomposition of symmetric matrices is at the heart of many computer vision algorithms.
Instability arises in the presence of eigenvalues that are close to each other.
We show that the Taylor expansion of the SVD gradient is theoretically equivalent to the gradient obtained using PI without relying on an iterative process.
arXiv Detail & Related papers (2021-04-08T15:04:15Z) - Learned Block Iterative Shrinkage Thresholding Algorithm for
Photothermal Super Resolution Imaging [52.42007686600479]
We propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network.
We show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters.
arXiv Detail & Related papers (2020-12-07T09:27:16Z) - One-Bit Compressed Sensing via One-Shot Hard Thresholding [7.594050968868919]
A problem of 1-bit compressed sensing is to estimate a sparse signal from a few binary measurements.
We present a novel and concise analysis that moves away from the widely used non-constrained notion of width.
arXiv Detail & Related papers (2020-07-07T17:28:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.