A Fast Random Sampling Algorithm for Sparsifying Matrices
We describe a simple random-sampling based procedure for producing sparse matrix approximations. Our procedure and analysis are extremely simple: the analysis uses nothing more than the Chernoff-Hoeffding bounds. Despite the simplicity, the approximation is comparable and sometimes better than previ...
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| creator | Arora, Sanjeev Hazan, Elad Kale, Satyen |
| description | We describe a simple random-sampling based procedure for producing sparse matrix approximations. Our procedure and analysis are extremely simple: the analysis uses nothing more than the Chernoff-Hoeffding bounds. Despite the simplicity, the approximation is comparable and sometimes better than previous work.
Our algorithm computes the sparse matrix approximation in a single pass over the data. Further, most of the entries in the output matrix are quantized, and can be succinctly represented by a bit vector, thus leading to much savings in space. |
| doi_str_mv | 10.1007/11830924_26 |
| format | Conference Proceeding |
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Our algorithm computes the sparse matrix approximation in a single pass over the data. Further, most of the entries in the output matrix are quantized, and can be succinctly represented by a bit vector, thus leading to much savings in space.</description><subject>Applied sciences</subject><subject>Eigenvector Computation</subject><subject>Error Parameter</subject><subject>Exact sciences and technology</subject><subject>Flows in networks. Combinatorial problems</subject><subject>Input Matrix</subject><subject>Lanczos Iteration</subject><subject>Mathematics</subject><subject>Numerical analysis</subject><subject>Numerical analysis. Scientific computation</subject><subject>Numerical approximation</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Sciences and techniques of general use</subject><subject>Unit Eigenvector</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540380443</isbn><isbn>3540380442</isbn><isbn>3540380450</isbn><isbn>9783540380450</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkLlOAzEURc0mkYRU_MA0FBQD79kzXsooIoAUhESgtjxewsBssqfJ35MoIFHd4lxdXR1CrhHuEEDcI0oGihaa8hMyZWUBTEJRwimZIEfMGSvUGZkrIf9Ywc7JBBjQXImCXZJpSl8AQIWiE6IW2cqkMXsznevbbGPaoam7bbZotn2sx882C33MNoOJqQ67A3kxY6ytT1fkIpgm-flvzsjH6uF9-ZSvXx-fl4t1binHMfeVMG5_xilf-iAdC9xzV3lZVgVHYWywwUtkQaCjSgjgTmHpHLXWhqr0bEZujruDSdY0IZrO1kkPsW5N3GlUXCrG5L53e-ylPeq2Puqq77-TRtAHcfqfOPYD_AFbFQ</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Arora, Sanjeev</creator><creator>Hazan, Elad</creator><creator>Kale, Satyen</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>A Fast Random Sampling Algorithm for Sparsifying Matrices</title><author>Arora, Sanjeev ; Hazan, Elad ; Kale, Satyen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c261t-eb7ad783d9e5ef8d3f6e6dbe85b4617acfcfe813f71d297706d915dd2cccfb5e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Eigenvector Computation</topic><topic>Error Parameter</topic><topic>Exact sciences and technology</topic><topic>Flows in networks. Combinatorial problems</topic><topic>Input Matrix</topic><topic>Lanczos Iteration</topic><topic>Mathematics</topic><topic>Numerical analysis</topic><topic>Numerical analysis. Scientific computation</topic><topic>Numerical approximation</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Sciences and techniques of general use</topic><topic>Unit Eigenvector</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Arora, Sanjeev</creatorcontrib><creatorcontrib>Hazan, Elad</creatorcontrib><creatorcontrib>Kale, Satyen</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Arora, Sanjeev</au><au>Hazan, Elad</au><au>Kale, Satyen</au><au>Zwick, Uri</au><au>Jansen, Klaus</au><au>Díaz, Josep</au><au>Rolim, José D. P.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Fast Random Sampling Algorithm for Sparsifying Matrices</atitle><btitle>Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques</btitle><date>2006</date><risdate>2006</risdate><spage>272</spage><epage>279</epage><pages>272-279</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540380443</isbn><isbn>3540380442</isbn><eisbn>3540380450</eisbn><eisbn>9783540380450</eisbn><abstract>We describe a simple random-sampling based procedure for producing sparse matrix approximations. Our procedure and analysis are extremely simple: the analysis uses nothing more than the Chernoff-Hoeffding bounds. Despite the simplicity, the approximation is comparable and sometimes better than previous work.
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| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 2006, p.272-279 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19689338 |
| source | Springer Books |
| subjects | Applied sciences Eigenvector Computation Error Parameter Exact sciences and technology Flows in networks. Combinatorial problems Input Matrix Lanczos Iteration Mathematics Numerical analysis Numerical analysis. Scientific computation Numerical approximation Operational research and scientific management Operational research. Management science Sciences and techniques of general use Unit Eigenvector |
| title | A Fast Random Sampling Algorithm for Sparsifying Matrices |
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