Numerical Linear Algebra and Optimization

Margaret H. Wright, Philip E. Gill, Walter Murray

ISBN 10: 1611976561 / ISBN 13: 9781611976564

Editore: Society for Industrial and Applied Mathematics,U.S., US, 2021

Riguardo questo articolo

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.

Codice articolo LU-9781611976564

Dati bibliografici

• Titolo: Numerical Linear Algebra and Optimization
• Editore: Society for Industrial and Applied Mathematics,U.S., US
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 1611976561
• ISBN 13: 9781611976564
• Rilegatura: Paperback
• Condizione: New
• Peso: 700 grammi

Descrizione articolo

Riassunto

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.