The university of Florida sparse matrix collection

By Timothy A. Davis, Yifan Hu. TOMS, 2011

4,362 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/e464550cdc5bfe80900afb8df94066c7ddb6b14d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Peter Richtárik
2. Robert Mansel Gower
3. Nicolas Loizou
4. Martin Takác
5. Haim Avron
6. Jinwoo Shin
7. Insu Han
8. Aydin Buluç

In Hardware and Architecture

1. Aydin Buluç
2. Leonid Oliker
3. Ariful Azad
4. John R. Gilbert
5. Charles E. Leiserson
6. Matteo Frigo
7. Jeremy T. Fineman
8. Robert Mansel Gower

Citations in the corpus

• Communication-Avoiding Parallel Sparse-Dense Matrix-Matrix Multiplication (2016, methodology): “Our final experiments test on three real-world matrices of different shapes from the University of Florida Sparse Matrix Collection [35].”
• Handbook of Big Data (2016, background): “Consider, the 1, 977, 885× 109, 900 Rucci1 matrix in the Florida Sparse Matrix Collection [8].”
• The GAP Benchmark Suite (2015, neutral): “• Web ( | V | =50.6M, | E | =1,949.4M, directed) is a web-crawl of the .sk domain ( sk-2005 ) [9].”
• Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition (2015, neutral): “We test the algorithms with varying iteration count q on three common datasets, SNAP/AMAZON0302 [22, 23], SNAP/EMAIL-ENRON [22, 35], and 20 NEWSGROUPS [36], computing column principal components in all cases.”
• An adaptive performance modeling tool for GPU architectures (2010, background): “Figure 10 shows the predicted and measured execution times for two sparse matrices from the University of Florida Sparse Matrix Collection [ 5 ].”
• Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks (2009, background): “The first ten matrices and Cage15 are from the University of Florida sparse matrix collection [12].”
• GraphMat: High performance graph analytics made productive (2015, methodology): “lenge for collaborative filtering [8], USA road network for California and Nevada [7] and the Livejournal, Wikipedia, Delaunay and Flickr graphs from the University of Florida Sparse Matrix collection [14]. We chose these datasets primarily to match those used in previous work [27, 13, 24] so that va”
• Notes on Birkhoff-von Neumann decomposition of doubly stochastic matrices (2016, positive): “We have selected all matrices with the following properties from the University of Florida Sparse Matrix (UFL) Collection [8]: square, has at least 5000 and at most 10000 rows, fully indecomposable, and there are at most 50 and at least 2 nonzeros per row.”
• Randomized Quasi-Newton Updates Are Linearly Convergent Matrix Inversion Algorithms (2016, methodology): “For our final batch of tests, we invert several sparse matrices from the Florida sparse matrix collection [9].”
• Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations (2016, methodology): “We tested the proposed algorithm for testing positive definiteness on real-world matrices from the University of Florida Sparse Matrix Collection [11], selecting various symmetric matrices.”