Numerical Linear Algebra and Optimization

This classic volume covers the fundamentals of two closely related linear systems (linear equations and least\-squares) and linear programming (optimizing a linear function subject to linear constraints). For each problem class, stable and efficient numerical algorithms intended for a finite\-precision environment are derived and analyzed. While linear algebra and optimization have made huge advances since this book first appeared in 1991, the fundamental principles have not changed. \n\nThese topics were rarely taught with a unified perspective, and, somewhat surprisingly, this remains true 30 years later. As a result, some of the material in this book can be difficult to find elsewhere―in particular, techniques for updating the LU factorization, descriptions of the simplex method applied to all\-inequality form, and the analysis of what happens when using an approximate inverse to solve Ax=b. \n\nNumerical Linear Algebra and Optimization is primarily a reference for students who want to learn about numerical techniques for solving linear systems and\/or linear programming using the simplex method; however, Chapters 6, 7, and 8 can be used as the text for an upper\-division course on linear least squares and linear programming. Understanding is enhanced by numerous exercises.