- William Perkins, Mark Tygert, and Rachel Ward,
"Computing the confidence levels for a root-mean-square test
of goodness-of-fit,"
*Applied Mathematics and Computation*, 217 (22): 9072-9084, 2011: pdf, ps.This article points out that, with the now widespread availability of computers, the original χ

^{2}statistic for testing goodness-of-fit no longer makes much sense. "χ^{2}and classical exact tests often wildly misreport significance; the remedy lies in computers," available in pdf, is a better place to start for the application-oriented. - Edouard Coakley, Vladimir Rokhlin, and Mark Tygert,
"A fast randomized algorithm for orthogonal projection,"
*SIAM Journal on Scientific Computing*, 33 (2): 849-868, 2011: pdf.This article can help accelerate interior-point methods for convex optimization, such as linear programming.

- Mark Tygert,
"Statistical tests for whether a given set of independent, identically
distributed draws comes from a specified probability density,"
*Proceedings of the National Academy of Sciences*, 107 (38): 16471-16476, 2010: pdf, ps.This article modifies and supplements tests of the Kolmogorov-Smirnov type (including Kuiper's).

- Mark Tygert,
"Fast algorithms for spherical harmonic expansions, III,"
*Journal of Computational Physics*, 229 (18): 6181-6192, 2010: pdf.This article simplifies the precomputations required for computing fast spherical harmonic transforms, complementing the approach taken in the article, "Fast algorithms for spherical harmonic expansions, II," available below.

- Vladimir Rokhlin, Arthur Szlam, and Mark Tygert,
"A randomized algorithm for principal component analysis,"
*SIAM Journal on Matrix Analysis and Applications*, 31 (3): 1100-1124, 2009: pdf.This article is now out-of-date; instead, please see Nathan Halko's, Per-Gunnar Martinsson's, and Joel Tropp's SIAM Review paper.

- Franco Woolfe, Edo Liberty, Vladimir Rokhlin, and Mark Tygert,
"A fast randomized algorithm for the approximation of matrices,"
*Applied and Computational Harmonic Analysis*, 25 (3): 335-366, 2008: pdf.This article provides a generally preferable alternative to the classical pivoted "

*QR*" decomposition algorithms (such as Gram-Schmidt or Householder) for the low-rank approximation of arbitrary matrices. Constructing a low-rank approximation is the core step in computing several of the greatest singular values and corresponding singular vectors of a matrix. - Vladimir Rokhlin and Mark Tygert,
"A fast randomized algorithm for overdetermined linear least-squares
regression,"
*Proceedings of the National Academy of Sciences*, 105 (36): 13212-13217, 2008: pdf.This article provides an algorithm for linear least-squares regression. When the regression is highly overdetermined, the algorithm is more efficient than the classical methods based on "

*QR*" decompositions. - Mark Tygert,
"Fast algorithms for spherical harmonic expansions, II,"
*Journal of Computational Physics*, 227 (8): 4260-4279, 2008: pdf.This article provides efficient algorithms for computing spherical harmonic transforms, largely superseding our first article on the subject, "Fast algorithms for spherical harmonic expansions."

- Edo Liberty, Franco Woolfe, Per-Gunnar Martinsson, Vladimir Rokhlin,
and Mark Tygert,
"Randomized algorithms for the low-rank approximation of matrices,"
*Proceedings of the National Academy of Sciences*, 104 (51): 20167-20172, 2007: pdf.This article surveys algorithms for the compression of matrices.

- Per-Gunnar Martinsson, Vladimir Rokhlin, and Mark Tygert,
"A randomized algorithm for the decomposition of matrices,"
*Applied and Computational Harmonic Analysis*, 30 (1): 47-68, 2011: pdf.This article provides details regarding the survey, "Randomized algorithms for the low-rank approximation of matrices," available above. "A randomized algorithm for the decomposition of matrices" is almost identical to our (better-known) technical report, "A randomized algorithm for the approximation of matrices," from 2006.

- Per-Gunnar Martinsson, Vladimir Rokhlin, and Mark Tygert,
"On interpolation and integration in finite-dimensional spaces
of bounded functions,"
*Communications in Applied Mathematics and Computational Science*, 1: 133-142, 2006: CAMCoS.This article reviews the fact that numerically stable formulae exist for interpolating any linear combination of

*n*bounded functions using the values of the linear combination at a certain collection of*n*points in the domain of the functions. The article also provides references to algorithms which determine these stable formulae at reasonably small computational expense. - Vladimir Rokhlin and Mark Tygert,
"Fast algorithms for spherical harmonic expansions,"
*SIAM Journal on Scientific Computing*, 27 (6): 1903-1928, 2006: pdf.This article is now largely (but not entirely) superseded by the paper, "Fast algorithms for spherical harmonic expansions, II," available above.