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Mat. Zametki, 1997, Volume 62, Issue 4, Pages 619–623 (Mi mz1644)  

This article is cited in 25 scientific papers (total in 25 papers)

Brief Communications

Pseudo-skeleton approximations by matrices of maximal volume

S. A. Goreinov, N. L. Zamarashkin, E. E. Tyrtyshnikov

Institute of Numerical Mathematics, Russian Academy of Sciences


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English version:
Mathematical Notes, 1997, 62:4, 515–519

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Received: 29.04.1997
Revised: 10.06.1997

Citation: S. A. Goreinov, N. L. Zamarashkin, E. E. Tyrtyshnikov, “Pseudo-skeleton approximations by matrices of maximal volume”, Mat. Zametki, 62:4 (1997), 619–623; Math. Notes, 62:4 (1997), 515–519

Citation in format AMSBIB
\by S.~A.~Goreinov, N.~L.~Zamarashkin, E.~E.~Tyrtyshnikov
\paper Pseudo-skeleton approximations by matrices of maximal volume
\jour Mat. Zametki
\yr 1997
\vol 62
\issue 4
\pages 619--623
\jour Math. Notes
\yr 1997
\vol 62
\issue 4
\pages 515--519

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    3. Rokhlin, V, “A RANDOMIZED ALGORITHM FOR PRINCIPAL COMPONENT ANALYSIS”, SIAM Journal on Matrix Analysis and Applications, 31:3 (2009), 1100  crossref  mathscinet  isi
    4. Caiafa C.F., Cichocki A., “Methods for Factorization and Approximation of Tensors by Partial Fiber Sampling”, 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (Camsap), IEEE, 2009, 73–76  crossref  isi
    5. Frederix K., Van Barel M., “Solving a Large Dense Linear System by Adaptive Cross Approximation”, J. Comput. Appl. Math., 234:11, SI (2010), 3181–3195  crossref  mathscinet  zmath  isi  elib
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    7. Deshpande A., Rademacher L., “Efficient Volume Sampling for Row/Column Subset Selection”, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, Annual IEEE Symposium on Foundations of Computer Science, IEEE Computer Soc, 2010, 329–338  crossref  mathscinet  isi
    8. Zhu X., Lin W., “Randomised Pseudo-Skeleton Approximation and its Application in Electromagnetics”, Electron. Lett., 47:10 (2011), 590–592  crossref  isi  elib
    9. Martinsson P.-G., Rokhlin V., Tygert M., “A Randomized Algorithm for the Decomposition of Matrices”, Appl. Comput. Harmon. Anal., 30:1 (2011), 47–68  crossref  mathscinet  zmath  isi  elib
    10. Civril A., Magdon-Ismail M., “Exponential Inapproximability of Selecting a Maximum Volume Sub-Matrix”, Algorithmica, 65:1 (2013), 159–176  crossref  mathscinet  zmath  isi
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    12. Wang Sh., Zhang Zh., “Improving Cur Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling”, J. Mach. Learn. Res., 14 (2013), 2729–2769  mathscinet  isi
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    14. Savostyanov D.V., “Quasioptimality of Maximum-Volume Cross Interpolation of Tensors”, Linear Alg. Appl., 458 (2014), 217–244  crossref  zmath  isi
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    19. Grasedyck L., Kriemann R., Loebbert Ch., Naegel A., Wittum G., Xylouris K., “Parallel Tensor Sampling in the Hierarchical Tucker Format”, Comput. Vis. Sci., 17:2 (2015), 67–78  crossref  mathscinet  isi  elib
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    22. Mach T., Reichel L., Van Barel M., Vandebril R., “Adaptive cross approximation for ill-posed problems”, J. Comput. Appl. Math., 303 (2016), 206–217  crossref  mathscinet  zmath  isi  elib  scopus
    23. Cichocki A., Lee N., Oseledets I., Anh-Huy Phan, Zhao Q., Mandic D.P., “Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions”, Found. Trends Mach. Learn., 9:4-5 (2016), I+  crossref  isi  scopus
    24. Bigoni D., Engsig-Karup A.P., Marzouk Y.M., “Spectral Tensor-Train Decomposition”, SIAM J. Sci. Comput., 38:4 (2016), A2405–A2439  crossref  mathscinet  zmath  isi  elib  scopus
    25. Georgieva I. Hofreither C., “An algorithm for low-rank approximation of bivariate functions using splines”, J. Comput. Appl. Math., 310 (2017), 80–91  crossref  mathscinet  zmath  isi  elib  scopus
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