Numerical Optimization with Computational Errors

Nonfiction, Science & Nature, Mathematics, Number Systems, Calculus
Cover of the book Numerical Optimization with Computational Errors by Alexander J. Zaslavski, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Alexander J. Zaslavski ISBN: 9783319309217
Publisher: Springer International Publishing Publication: April 22, 2016
Imprint: Springer Language: English
Author: Alexander J. Zaslavski
ISBN: 9783319309217
Publisher: Springer International Publishing
Publication: April 22, 2016
Imprint: Springer
Language: English

This book studies the approximate solutions of optimization problems in the presence of computational errors. A number of results are presented on the convergence behavior of algorithms in a Hilbert space; these algorithms are examined taking into account computational errors. The author illustrates that algorithms generate a good approximate solution, if computational errors are bounded from above by a small positive constant. Known computational errors  are examined with the aim of determining an approximate solution. Researchers and students interested in the optimization theory and its applications will find this book instructive and informative.

 

This monograph contains 16 chapters; including a chapters devoted to the subgradient projection algorithm, the mirror descent algorithm, gradient projection algorithm, the Weiszfelds method, constrained convex minimization problems, the convergence of a proximal point method in a Hilbert space, the continuous subgradient method, penalty methods and Newton’s method.

  

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book studies the approximate solutions of optimization problems in the presence of computational errors. A number of results are presented on the convergence behavior of algorithms in a Hilbert space; these algorithms are examined taking into account computational errors. The author illustrates that algorithms generate a good approximate solution, if computational errors are bounded from above by a small positive constant. Known computational errors  are examined with the aim of determining an approximate solution. Researchers and students interested in the optimization theory and its applications will find this book instructive and informative.

 

This monograph contains 16 chapters; including a chapters devoted to the subgradient projection algorithm, the mirror descent algorithm, gradient projection algorithm, the Weiszfelds method, constrained convex minimization problems, the convergence of a proximal point method in a Hilbert space, the continuous subgradient method, penalty methods and Newton’s method.

  

More books from Springer International Publishing

Cover of the book Financial Modelling with Forward-looking Information by Alexander J. Zaslavski
Cover of the book Renewable Biofuels by Alexander J. Zaslavski
Cover of the book Advanced Microsystems for Automotive Applications 2018 by Alexander J. Zaslavski
Cover of the book Spatio-Temporal Databases by Alexander J. Zaslavski
Cover of the book Advanced Microsystems for Automotive Applications 2015 by Alexander J. Zaslavski
Cover of the book Advances in Spatial and Temporal Databases by Alexander J. Zaslavski
Cover of the book Science of Crystal Structures by Alexander J. Zaslavski
Cover of the book Knowledge and Time by Alexander J. Zaslavski
Cover of the book Technology and Adolescent Mental Health by Alexander J. Zaslavski
Cover of the book The Changing Epistemic Governance of European Education by Alexander J. Zaslavski
Cover of the book Full Employment and Social Justice by Alexander J. Zaslavski
Cover of the book Graph-Based Modelling in Engineering by Alexander J. Zaslavski
Cover of the book Political Representation in France and Germany by Alexander J. Zaslavski
Cover of the book Systems for Big Graph Analytics by Alexander J. Zaslavski
Cover of the book Bacterial Pathogens and Their Virulence Factors by Alexander J. Zaslavski
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy