M-IHS: an accelerated randomized preconditioning method avoiding costly matrix decompositions
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Publication:6083984
DOI10.1016/j.laa.2023.08.014zbMath1525.65022arXiv1912.03514OpenAlexW3107854375MaRDI QIDQ6083984
Orhan Arıkan, Ibrahim K. Ozaslan, Mert Pilanci
Publication date: 31 October 2023
Published in: Linear Algebra and its Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1912.03514
Numerical solutions to overdetermined systems, pseudoinverses (65F20) Iterative numerical methods for linear systems (65F10) Randomized algorithms (68W20) Preconditioners for iterative methods (65F08)
Cites Work
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- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Faster least squares approximation
- Analysis of the symmetric Lanczos algorithm with reorthogonalization methods
- Regularization tools: A Matlab package for analysis and solution of discrete ill-posed problems
- A survey of truncated-Newton methods
- Sketching meets random projection in the dual: a provable recovery algorithm for big and high-dimensional data
- Preconditioning techniques for large linear systems: A survey
- On Krylov projection methods and Tikhonov regularization
- Adaptive restart for accelerated gradient schemes
- IR tools: a MATLAB package of iterative regularization methods and large-scale test problems
- Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems
- Iterative Hessian sketch: Fast and accurate solution approximation for constrained least-squares
- LSRN: A Parallel Iterative Solver for Strongly Over- or Underdetermined Systems
- Tabulation-Based 5-Independent Hashing with Applications to Linear Probing and Second Moment Estimation
- Computational Advertising: Techniques for Targeting Relevant Ads
- Newton Sketch: A Near Linear-Time Optimization Algorithm with Linear-Quadratic Convergence
- Randomized Sketches of Convex Programs With Sharp Guarantees
- Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix
- A fast randomized algorithm for overdetermined linear least-squares regression
- Almost Optimal Explicit Johnson-Lindenstrauss Families
- Randomized Algorithms for Matrices and Data
- Blendenpik: Supercharging LAPACK's Least-Squares Solver
- Sparser Johnson-Lindenstrauss Transforms
- Extensions of Lipschitz mappings into a Hilbert space
- LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
- Algorithms for the regularization of ill-conditioned least squares problems
- Nearly Tight Oblivious Subspace Embeddings by Trace Inequalities
- Faster Kernel Ridge Regression Using Sketching and Preconditioning
- Optimal Approximate Matrix Product in Terms of Stable Rank
- Inverse problems in biomedical imaging: modeling and methods of solution
- Computational Methods for Inverse Problems
- Choosing the Forcing Terms in an Inexact Newton Method
- Sharper Bounds for Regularized Data Fitting
- An investigation of Newton-Sketch and subsampled Newton methods
- Lower Bounds for Oblivious Subspace Embeddings
- Discrete Inverse Problems
- Some methods of speeding up the convergence of iteration methods
- Compressed sensing
- The Chebyshev iteration revisited
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