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math.NA

Papers filed under math.NA on arXiv, each one already summarized by Paperlayer. Open any of them to read the summary beside the original PDF, with every point linked to the line, figure, or table it came from.

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901 to 952 of 952

  1. Blockwise Stabilized Adaptive Cubic Regularization with Subsolvers via Recurrence

    Rodion Podorozhny

    cs.LGmath.NAarXiv:2608.22129v22026

  2. A mesh-free multiresolution deep energy method with phase-field modeling of brittle fracture

    Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh +4

    cs.LGmath.NAarXiv:2608.24126v12026

  3. Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints

    Laurent Lessard, Benjamin Recht, Andrew Packard

    math.OCeess.SYmath.NAarXiv:1408.3595v72014

  4. Inertial Manifold Neural Operator for Dissipative Time-Dependent Partial Differential Equations

    Xiaoyang Xie, Clarence W. Rowley

    math.NAcs.LGmath.DSarXiv:2608.23546v12026

  5. A literature survey of low-rank tensor approximation techniques

    Lars Grasedyck, Daniel Kressner, Christine Tobler

    math.NAquant-pharXiv:1302.7121v12013

  6. DySCo: Dynamically consistent data-driven downscaling of extremes in climate projections

    S. Stamatelopoulos, M. Wang, I. Lopez-Gomez +5

    cs.LGmath.NAphysics.ao-pharXiv:2608.21998v12026

  7. PageRank beyond the Web

    David F. Gleich

    cs.SIcs.CEmath.NAarXiv:1407.5107v12014

  8. Gaussian process learning with flow map refinement for parameter estimation in dynamical systems

    Yue Hao, Dongwei Ye

    cs.LGcs.CEmath.NAarXiv:2608.22324v12026

  9. A Neural Score-Based Particle Method for the Vlasov-Maxwell-Landau System

    Vasily Ilin, Jingwei Hu

    math.NAcs.LGmath.AParXiv:2603.25832v12026

  10. DOLFIN: Automated Finite Element Computing

    Anders Logg, Garth N. Wells

    cs.MSmath.NAarXiv:1103.6248v12011

  11. A new difference scheme for the time fractional diffusion equation

    A. A. Alikhanov

    math.NAmath-pharXiv:1404.5221v32014

  12. Mirror descent algorithms with logarithmic barriers

    Alberto De Marchi, Yura Malitsky, Adrien B. Taylor

    math.OCcs.LGmath.NAarXiv:2608.22834v12026

  13. hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition

    Ehsan Kharazmi, Zhongqiang Zhang, George Em Karniadakis

    cs.NEcs.LGmath.NAarXiv:2003.05385v12020

  14. Stable Architectures for Deep Neural Networks

    Eldad Haber, Lars Ruthotto

    cs.LGmath.NAmath.OCarXiv:1705.03341v32017

  15. An Open-Source Pseudo-Spectral Solver for Idealized Korteweg-de Vries Soliton Simulations

    Dasapta E. Irawan, Sandy H. S. Herho, Faruq Khadami +5

    nlin.PSmath.NAphysics.ao-pharXiv:2601.12029v32026

  16. Data-Driven Dynamic Algorithm Dispatch with Large Language Models

    Rushil Shah, Emmanuel Lujan, Rabab Alomairy +1

    cs.AIcs.CEmath.NAarXiv:2608.21584v12026

  17. PDE-Net: Learning PDEs from Data

    Zichao Long, Yiping Lu, Xianzhong Ma +1

    math.NAcs.LGcs.NEarXiv:1710.09668v22017

  18. Physics-Informed Neural Operator for Learning Partial Differential Equations

    Zongyi Li, Hongkai Zheng, Nikola Kovachki +5

    cs.LGmath.NAarXiv:2111.03794v42021

  19. Iterative Bregman Projections for Regularized Transportation Problems

    Jean-David Benamou, Guillaume Carlier, Marco Cuturi +2

    math.NAmath.AParXiv:1412.5154v12014

  20. Pseudo Numerical Methods for Diffusion Models on Manifolds

    Luping Liu, Yi Ren, Zhijie Lin +1

    cs.CVcs.LGmath.NAarXiv:2202.09778v22022

  21. Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

    Maziar Raissi

    stat.MLcs.LGmath.AParXiv:1801.06637v12018

  22. Signal Recovery from Incomplete and Inaccurate Measurements via Regularized Orthogonal Matching Pursuit

    Deanna Needell, Roman Vershynin

    math.NAarXiv:0712.1360v12007

  23. Survey of multifidelity methods in uncertainty propagation, inference, and optimization

    Benjamin Peherstorfer, Karen Willcox, Max Gunzburger

    math.NAstat.COstat.MEarXiv:1806.10761v12018

  24. Compressed Sensing with Coherent and Redundant Dictionaries

    Emmanuel J. Candes, Yonina C. Eldar, Deanna Needell +1

    math.NAcs.ITarXiv:1005.2613v32010

  25. The Little Engine that Could: Regularization by Denoising (RED)

    Yaniv Romano, Michael Elad, Peyman Milanfar

    cs.CVmath.NAarXiv:1611.02862v32016

  26. A randomized Kaczmarz algorithm with exponential convergence

    Thomas Strohmer, Roman Vershynin

    math.NAmath.PRarXiv:math/0702226v12007

  27. Uniform Uncertainty Principle and signal recovery via Regularized Orthogonal Matching Pursuit

    Deanna Needell, Roman Vershynin

    math.NAarXiv:0707.4203v42007

  28. A Simpler Approach to Matrix Completion

    Benjamin Recht

    cs.ITmath.NAmath.OCarXiv:0910.0651v22009

  29. Moment Tensor Potentials: a class of systematically improvable interatomic potentials

    Alexander V. Shapeev

    physics.comp-phcond-mat.mtrl-scimath.NAarXiv:1512.06054v22015

  30. Advanced Linear Algebra with Applications - Part I (Numerical linear algebra for PDEs, machine learning, and data assimilation)

    Victorita Dolean, Jemima Tabeart

    math.NAcs.LGarXiv:2608.21234v12026

  31. Anchored Regularized Direct Least Squares (ARDLS): Integrating Established Prioritization Operators for Priority Elicitation in the Analytic Hierarchy Process

    Kevin Kam Fung Yuen

    math.OCcs.AImath.NAarXiv:2608.21187v12026

  32. An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems

    Manya V. Afonso, José M. Bioucas-Dias, Mário A. T. Figueiredo

    math.OCmath.NAarXiv:0912.3481v12009

  33. Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets

    Sifan Wang, Hanwen Wang, Paris Perdikaris

    cs.LGmath.NAstat.MLarXiv:2103.10974v12021

  34. Characterizing possible failure modes in physics-informed neural networks

    Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe +2

    cs.LGcs.AImath.NAarXiv:2109.01050v22021

  35. PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming

    Emmanuel J. Candes, Thomas Strohmer, Vladislav Voroninski

    cs.ITmath.NAarXiv:1109.4499v12011

  36. Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis

    A. Cichocki, D. Mandic, A-H. Phan +4

    math.NAarXiv:1403.4462v12014

  37. Global Convergence of ADMM in Nonconvex Nonsmooth Optimization

    Yu Wang, Wotao Yin, Jinshan Zeng

    math.OCmath.NAarXiv:1511.06324v82015

  38. Most tensor problems are NP-hard

    Christopher Hillar, Lek-Heng Lim

    cs.CCmath.NAarXiv:0911.1393v52009

  39. Phase Retrieval via Wirtinger Flow: Theory and Algorithms

    Emmanuel Candes, Xiaodong Li, Mahdi Soltanolkotabi

    cs.ITmath.FAmath.NAarXiv:1407.1065v32014

  40. Recursive Flow Matching

    Jiahe Huang, Sihan Xu, Sharvaree Vadgama +1

    cs.LGcs.AIcs.CVarXiv:2605.26535v12026

  41. On Dynamic Mode Decomposition: Theory and Applications

    Jonathan H. Tu, Clarence W. Rowley, Dirk M. Luchtenburg +2

    math.NAphysics.flu-dynarXiv:1312.0041v12013

  42. Solving high-dimensional partial differential equations using deep learning

    Jiequn Han, Arnulf Jentzen, Weinan E

    math.NAcs.LGmath.OCarXiv:1707.02568v32017

  43. Iterative Hard Thresholding for Compressed Sensing

    Thomas Blumensath, Mike E. Davies

    cs.ITmath.NAarXiv:0805.0510v12008

  44. DGM: A deep learning algorithm for solving partial differential equations

    Justin Sirignano, Konstantinos Spiliopoulos

    q-fin.MFmath.NAq-fin.CParXiv:1708.07469v52017

  45. Introduction to the non-asymptotic analysis of random matrices

    Roman Vershynin

    math.PRmath.FAmath.NAarXiv:1011.3027v72010

  46. The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices

    Zhouchen Lin, Minming Chen, Yi Ma

    math.OCeess.SYmath.NAarXiv:1009.5055v32010

  47. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions

    Nathan Halko, Per-Gunnar Martinsson, Joel A. Tropp

    math.NAmath.PRarXiv:0909.4061v22009

  48. Fourier Neural Operator for Parametric Partial Differential Equations

    Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli +4

    cs.LGmath.NAarXiv:2010.08895v32020

  49. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples

    D. Needell, J. A. Tropp

    math.NAcs.ITarXiv:0803.2392v22008

  50. Deep Embedded Multiplicative DMD for Algebra-Preserving Koopman Learning

    Kelan Gray, Finlay Brown, Nicolas Boullé +1

    cs.LGmath.DSmath.NAarXiv:2606.05131v12026

  51. AutoSR: Automatic Symbolic Regression by Searching Research States

    Kejia Zhang, Youran Sun, Xinyu Ren +2

    cs.SCcs.AIcs.LGarXiv:2608.16876v12026

  52. MiNO: Cotangent-bundle propagator learning for PDEs

    Gnankan Landry Regis N'guessan, Bum Jun Kim

    cs.LGcs.CEmath.NAarXiv:2608.15187v12026