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Book
Convex sets
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Year: 1964 Publisher: New York (N.Y.): McGraw-Hill,

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Convex sets


Book
Konvexe Mengen
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Year: 1968 Publisher: Mannheim: Bibliographisches Institut,

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Convex sets


Book
Alternating projection methods
Authors: ---
ISBN: 1611971934 9781611971934 Year: 2011 Publisher: Philadelphia: Society for industrial and applied mathematics,

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Projection --- Algorithms --- Convex sets


Book
Convex sets
Author:
Year: 1964 Publisher: New York : McGraw-Hill,

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Blaschke's rolling theorem in Rn
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ISBN: 082182466X Year: 1989 Publisher: Providence (R.I.): American Mathematical Society

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Convexity and optimization in R [superscript n]
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ISBN: 0471352810 Year: 2001 Publisher: New York Wiley

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Book
Lectures on convex sets
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ISBN: 9789811203510 9789811202117 9811202117 9811203512 Year: 2020 Publisher: New Jersey : World Scientific,

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"The exposition is self-contained, detailed and provides multiple cross-references, that makes the book accessible to a large audience An essential part of the text is adapted from various research articles, never presented before in a textbook format The book has a multidisciplinary nature: it can be useful to specialists in geometry, convex analysis, operations research, and optimization The new edition contains new chapters and additional exercises with respective solutions Despite the presence of a large number of monographs on convex sets, there are quite a few textbooks on this topic. This book is to the level of graduate study, with higher degree of complexity and essentially more research-related results and references"--


Book
Geometry of convex sets : solutions manual
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ISBN: 9781119184188 1119184185 Year: 2016 Publisher: Hoboken: Wiley,

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Book
Geometry of convex sets
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ISBN: 9781119022664 1119022665 Year: 2016 Publisher: Hoboken: Wiley,

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Book
Optimization Models
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ISBN: 9781107050877 1107050871 Year: 2014 Publisher: Cambridge Cambridge University Press

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Emphasizing practical understanding over the technicalities of specific algorithms, this elegant textbook is an accessible introduction to the field of optimization, focusing on powerful and reliable convex optimization techniques. Students and practitioners will learn how to recognize, simplify, model and solve optimization problems - and apply these principles to their own projects. A clear and self-contained introduction to linear algebra demonstrates core mathematical concepts in a way that is easy to follow, and helps students to understand their practical relevance. Requiring only a basic understanding of geometry, calculus, probability and statistics, and striking a careful balance between accessibility and rigor, it enables students to quickly understand the material, without being overwhelmed by complex mathematics. Accompanied by numerous end-of-chapter problems, an online solutions manual for instructors, and relevant examples from diverse fields including engineering, data science, economics, finance, and management, this is the perfect introduction to optimization for undergraduate and graduate students.

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