Convexity and its Applications in Discrete and Continuous Optimization - Basu, Amitabh; - Prospero Internetes Könyváruház

Convexity and its Applications in Discrete and Continuous Optimization

 
Kiadó: Cambridge University Press
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GBP 54.99
Becsült forint ár:
28 119 Ft (26 780 Ft + 5% áfa)
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22 495 (21 424 Ft + 5% áfa )
Kedvezmény(ek): 20% (kb. 5 624 Ft)
A kedvezmény érvényes eddig: 2024. december 31.
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Rövid leírás:

A unified introduction to continuous and discrete aspects of convexity, including recent developments in optimization and 300+ exercises.

Hosszú leírás:
Using a pedagogical, unified approach, this book presents both the analytic and combinatorial aspects of convexity and its applications in optimization. On the structural side, this is done via an exposition of classical convex analysis and geometry, along with polyhedral theory and geometry of numbers. On the algorithmic/optimization side, this is done by the first ever exposition of the theory of general mixed-integer convex optimization in a textbook setting. Classical continuous convex optimization and pure integer convex optimization are presented as special cases, without compromising on the depth of either of these areas. For this purpose, several new developments from the past decade are presented for the first time outside technical research articles: discrete Helly numbers, new insights into sublinear functions, and best known bounds on the information and algorithmic complexity of mixed-integer convex optimization. Pedagogical explanations and more than 300 exercises make this book ideal for students and researchers.

'Written by one of the most brilliant researchers in the field, this book provides an elegant, rigorous and original presentation of the theory of convexity, describing in a unified way its use in continuous and discrete optimization, and also covering some very recent advancements in these areas.' Marco Di Summa, University of Padua
Tartalomjegyzék:
1. Preliminaries; Part I. Structural Aspects: 2. Convex sets; 3. Convex functions; 4. Geometry of numbers; Part II. Optimization: 5. Ingredients of mathematical optimization; 6. Complexity of convex optimization with integer variables; 7. Certificates and duality; Hints to selected exercises; References; Index.