Buch, Englisch, 256 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 423 g
Reihe: CMS Books in Mathematics
A Treatise on Algorithmic Science and the Karmarkar Revolution
Buch, Englisch, 256 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 423 g
Reihe: CMS Books in Mathematics
ISBN: 978-1-4419-3061-3
Verlag: Springer
In 1984, N. Karmarkar published a seminal paper on algorithmic linear programming. During the subsequent decade, it stimulated a huge outpouring of new algorithmic results by researchers world-wide in many areas of mathematical programming and numerical computation. This book gives an overview of the resulting, dramatic reorganization that has occurred in one of these areas: algorithmic differentiable optimization and equation-solving, or, more simply, algorithmic differentiable programming. The book is aimed at readers familiar with advanced calculus, numerical analysis, in particular numerical linear algebra, the theory and algorithms of linear and nonlinear programming, and the fundamentals of computer science, in particular, computer programming and the basic models of computation and complexity theory.
"Very fine monograph...filled with great insights."
-Joseph F. Traub, Columbia University
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
- Mathematik | Informatik Mathematik Operations Research
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
Weitere Infos & Material
Foundations.- The Karmarkar Revolution.- The Newton-Cauchy Method.- Euler-Newton and Lagrange-NC Methods.- Lessons from One Dimension.- A Misleading Paradigm.- CG and the Line Search.- Gilding the Nelder—Mead Lily.- Choosing the Right Diagonal Scale.- Historical Parallels.- LP from the Newton-Cauchy Perspective.- Diagonal Metrics and the QC Method.- Linear Programming Post-Karmarkar.- LP from the Euler-Newton Perspective.- Log-Barrier Transformations.- Karmarkar Potentials and Algorithms.- Algorithmic Science.- Algorithmic Principles.- Multialgorithms: A New Paradigm.- An Emerging Discipline.
