Employing a factorization based on the least significant singular values provides a matrix approximation with many surprisingly useful properties. This Reverse Singular Value Decomposition, RSVD, is also referred to as Subordinate Component Analysis, SCA, to distinguish it from Principal Component Analysis. ... 더 읽어보기 >>

High resolution measurements of the depth of the mold for the United States one cent coin provide an interesting data set.... 더 읽어보기 >>

ContentsTim DavisSparse Matrix VisualizationsMusic VisualizationsMetastaseisBlue MondayElectrodoodleMorning Has BrokenBach Toccata and FugueNotesArt StudioTim DavisAt the recent SIAM conference on... 더 읽어보기 >>

The Tektronix 4081 minicomputer at Argonne National Laboratory in the summers of 1977 and '78 played an important role in the early development of MATLAB.... 더 읽어보기 >>

I am surprised when many of the singular values of a nonsymmetric variant of the Hilbert matrix turn out to be nearly equal to $\pi$. The explanation involves the Fourier series for a square wave.... 더 읽어보기 >>

Magic Stars are a little like Magic Squares. My grandson was introduced to them in his junior high school math class.... 더 읽어보기 >>

The Rosser matrix is a classic matrix eigenvalue test problem.... 더 읽어보기 >>