4.6 Numerical methods of linear algebra A Machine-Oriented Technique for the Complete Solution of Linear Systems, Eric J. Nelson, 8:3, 1977, 161-164 Harvesting a Grizzly Bear Population, Michael Caulfield and John Kent and Daniel McCaffery, 17:1, 1986, 34-46, 4.1, 9.10 Why Should We Pivot in Gaussian Elimination?, Edward Rozema, 19:1, 1988, 63-72, 4.1 Connecting the Dots Parametrically: An Alternative to Cubic Splines, Wilbur J. Hildebrand, 21:3, 1990, 208-215, 5.6.1, 9.6 Round-off, Batting Averages, and Ill-Conditioning, Edward Rozema, 25:4, 1994, 314-317, C, 4.1 A Simple Estimate of the Condition Number of a Linear System, Heinrich W. Guggenheimer, Alan S. Edelman, and Charles R. Johnson, 26:1, 1995, 2-5, 4.5 A Singularly Valuable Decomposition: The SVD of a Matrix, Dan Kalman, 27:1, 1996, 2-23 Of Memories, Neurons, and Rank-One Corrections, Kevin G. Kirby, 28:1, 1997, 2-19, 8.4 The Generalized Spectral Decomposition of a Linear Operator, Garret Sobczyk, 28:1, 1997, 27-38, 9.4 Gaussian Elimination and Dynamical Systems, Kathie Yerion, 28:2, 1997, 89-97, 9.6 A Fresh Approach to the Singular Value Decomposition, Colm Mulcahy and John Rossi, 29:3, 1998, 199-207 If ItŐs in the Textbook, It Must Be True, Donald A. Teets, 31:4, 2000, 307-308, F, 6.6 Surface Approximation and Interpolation via Matrix SVD, Andrew E. Long and Clifford A. Long, 32:1, 2001, 20-25 Obtaining the QR Decomposition by Pairs of Row and Column Operations, Sidney H. Kung, 33:4, 2002, 320-321, C, 4.1