Single Pass Spectral Sparsification in Dynamic Streams
From MaRDI portal
Publication:2968162
DOI10.1137/141002281zbMath1359.68295arXiv1407.1289OpenAlexW2591972614MaRDI QIDQ2968162
Yin Tat Lee, Aaron Sidford, C. N. Musco, C. P. Musco, Michael Kapralov
Publication date: 10 March 2017
Published in: SIAM Journal on Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1407.1289
Related Items (20)
Quantum Speedup for Graph Sparsification, Cut Approximation, and Laplacian Solving ⋮ Graph coarsening: from scientific computing to machine learning ⋮ Maximum Matching in Turnstile Streams ⋮ Constructing Linear-Sized Spectral Sparsification in Almost-Linear Time ⋮ Graph sketching and streaming: new approaches for analyzing massive graphs ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Single Pass Spectral Sparsification in Dynamic Streams ⋮ Unnamed Item ⋮ Single-pass streaming algorithms to partition graphs into few forests ⋮ Approximate F_2-Sketching of Valuation Functions ⋮ Dynamic graph stream algorithms in \(o(n)\) space ⋮ Unnamed Item ⋮ Correlation clustering in data streams ⋮ Unnamed Item ⋮ Querying a Matrix Through Matrix-Vector Products. ⋮ Better streaming algorithms for the maximum coverage problem ⋮ Estimating Leverage Scores via Rank Revealing Methods and Randomization
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Spectral sparsification in the semi-streaming setting
- User-friendly tail bounds for sums of random matrices
- Pseudorandom generators for space-bounded computation
- On graph problems in a semi-streaming model
- Spectral sparsification via random spanners
- Spectral Sparsification in Dynamic Graph Streams
- Approximate Sparse Recovery: Optimizing Time and Measurements
- Nearly Linear Time Algorithms for Preconditioning and Solving Symmetric, Diagonally Dominant Linear Systems
- Spanners and sparsifiers in dynamic streams
- Single Pass Spectral Sparsification in Dynamic Streams
- Uniform Sampling for Matrix Approximation
- Spectral Sparsification of Graphs
- Low-Rank Approximation and Regression in Input Sparsity Time
- Streaming and fully dynamic centralized algorithms for constructing and maintaining sparse spanners
- Improved Approximation Guarantees for Weighted Matching in the Semi-streaming Model
- Stable distributions, pseudorandom generators, embeddings, and data stream computation
- Graph Sparsification in the Semi-streaming Model
- Numerical linear algebra in the streaming model
- Probability Inequalities for Sums of Bounded Random Variables
- A Nearly-m log n Time Solver for SDD Linear Systems
- Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression
- Graph Sparsification by Effective Resistances
This page was built for publication: Single Pass Spectral Sparsification in Dynamic Streams