scientific article; zbMATH DE number 7049764
zbMath1484.68341arXiv1705.05067MaRDI QIDQ4633059
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Publication date: 2 May 2019
Full work available at URL: https://arxiv.org/abs/1705.05067
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
matrix approximationonline convex optimizationfrequent directionsonline matrix sketchingonline Newton algorithm
Numerical mathematical programming methods (65K05) Convex programming (90C25) Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Online algorithms; streaming algorithms (68W27) Numerical methods for low-rank matrix approximation; matrix compression (65F55)
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Cites Work
- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- Logarithmic regret algorithms for online convex optimization
- Fast dimension reduction using Rademacher series on dual BCH codes
- On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix
- A simplified neuron model as a principal component analyzer
- Finding repeated elements
- Database-friendly random projections: Johnson-Lindenstrauss with binary coins.
- Sub-sampled Newton methods
- A note on element-wise matrix sparsification via a matrix-valued Bernstein inequality
- The matrix ridge approximation: algorithms and applications
- Sequential Karhunen-Loeve basis extraction and its application to images
- Frequent Directions: Simple and Deterministic Matrix Sketching
- Improved Practical Matrix Sketching with Guarantees
- Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
- An Almost Optimal Unrestricted Fast Johnson-Lindenstrauss Transform
- Computational Advertising: Techniques for Targeting Relevant Ads
- Newton Sketch: A Near Linear-Time Optimization Algorithm with Linear-Quadratic Convergence
- Randomized Algorithms for Matrices and Data
- Online Learning and Online Convex Optimization
- Low-Rank Approximation and Regression in Input Sparsity Time
- Sparser Johnson-Lindenstrauss Transforms
- Extensions of Lipschitz mappings into a Hilbert space
- Fast computation of low-rank matrix approximations
- A Fast Random Sampling Algorithm for Sparsifying Matrices
- Relative-Error $CUR$ Matrix Decompositions
- Fast monte-carlo algorithms for finding low-rank approximations
- Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication
- Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix
- An Introduction to Matrix Concentration Inequalities
- Introduction to Online Convex Optimization
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