Description

Book Synopsis
Numerical Linear Algebra and Optimization covers the fundamentals of closely related topics: 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. In 1991, when the book first appeared, these topics were rarely taught with a unified perspective, and, somewhat surprisingly, this remains true almost 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.

This book is appropriate for students who want to learn about numerical techniques for solving linear systems and/or linear programming using the simplex method.

Numerical Linear Algebra and Optimization

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    A Paperback / softback by Philip E. Gill, Walter Murray, Margaret H. Wright

    £75.65

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      Book details

      Publisher Society for Industrial & Applied Mathematics,U.S.
      Published 30 June 2021
      ISBN-13 9781611976564
      978-1611976564
      ISBN-10 1611976561
      Also in:
      Applied mathematics Linear programming

      Description

      Book Synopsis
      Numerical Linear Algebra and Optimization covers the fundamentals of closely related topics: 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. In 1991, when the book first appeared, these topics were rarely taught with a unified perspective, and, somewhat surprisingly, this remains true almost 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.

      This book is appropriate for students who want to learn about numerical techniques for solving linear systems and/or linear programming using the simplex method.

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