Lectures on Convex Optimization

 
 
Springer (Verlag)
  • 2. Auflage
  • |
  • erschienen im November 2018
 
  • Buch
  • |
  • Hardcover
  • |
  • XXIII, 589 Seiten
978-3-319-91577-7 (ISBN)
 
This book provides a comprehensive, modern introduction to convex optimization, a field that is becoming increasingly important in applied mathematics, economics and finance, engineering, and computer science, notably in data science and machine learning.



Written by a leading expert in the field, this book includes recent advances in the algorithmic theory of convex optimization, naturally complementing the existing literature. It contains a unified and rigorous presentation of the acceleration techniques for minimization schemes of first- and second-order. It provides readers with a full treatment of the smoothing technique, which has tremendously extended the abilities of gradient-type methods. Several powerful approaches in structural optimization, including optimization in relative scale and polynomial-time interior-point methods, are also discussed in detail.

Researchers in theoretical optimization as well as professionals working on optimization problems will find this book very useful. It presents many successful examples of how to develop very fast specialized minimization algorithms. Based on the author's lectures, it can naturally serve as the basis for introductory and advanced courses in convex optimization for students in engineering, economics, computer science and mathematics.
Book
2nd ed. 2018. 2018
  • Englisch
  • Cham
  • |
  • Schweiz
Springer International Publishing
  • Für höhere Schule und Studium
  • |
  • Für Beruf und Forschung
  • Überarbeitete Ausgabe
  • 1 s/w Abbildung
  • |
  • Bibliographie
  • Höhe: 241 mm
  • |
  • Breite: 161 mm
  • |
  • Dicke: 43 mm
  • 1068 gr
978-3-319-91577-7 (9783319915777)
10.1007/978-3-319-91578-4
weitere Ausgaben werden ermittelt
¿Yurii Nesterov is a well-known specialist in optimization. He is an author of pioneering works related to fast gradient methods, polynomial-time interior-point methods, smoothing technique, regularized Newton methods, and others. He is a winner of several prestigious international prizes, including George Danzig prize (2000), von Neumann Theory prize (2009), SIAM Outstanding Paper Award (20014), and Euro Gold Medal (2016).
Introduction.- Part I Black-Box Optimization.- 1 Nonlinear Optimization.- 2 Smooth Convex Optimization.- 3 Nonsmooth Convex Optimization.- 4 Second-Order Methods.- Part II Structural Optimization.- 5 Polynomial-time Interior-Point Methods.- 6 Primal-Dual Model of Objective Function.- 7 Optimization in Relative Scale.- Bibliographical Comments.- Appendix A. Solving some Auxiliary Optimization Problems.- References.- Index.
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