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  3. computational geometry

LATIN 2020: Theoretical Informatics

Posted By: AvaxGenius
Date: 3 Dec 2020 05:50:32
LATIN 2020: Theoretical Informatics

LATIN 2020: Theoretical Informatics: 14th Latin American Symposium, São Paulo, Brazil, January 5-8, 2021, Proceedings by Yoshiharu Kohayakawa
English | PDF | 2020 | 653 Pages | ISBN : 3030617912 | 24.8 MB

This book constitutes the refereed proceedings of the 14th Latin American Symposium on Theoretical Informatics, LATIN 2020, held in Sao Paulo, Brazil, in January 2021.
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Algorithm Engineering: Selected Results and Surveys (Repost)

Posted By: AvaxGenius
Date: 9 Aug 2020 15:55:32
Algorithm Engineering: Selected Results and Surveys (Repost)

Algorithm Engineering: Selected Results and Surveys by Lasse Kliemann
English | PDF | 2016 | 428 Pages | ISBN : 3319494864 | 10.73 MB

Algorithm Engineering is a methodology for algorithmic research that combines theory with implementation and experimentation in order to obtain better algorithms with high practical impact. Traditionally, the study of algorithms was dominated by mathematical (worst-case) analysis. In Algorithm Engineering, algorithms are also implemented and experiments conducted in a systematic way, sometimes resembling the experimentation processes known from fields such as biology, chemistry, or physics. This helps in counteracting an otherwise growing gap between theory and practice.
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Computational Geometry: Algorithms and Applications

Posted By: AvaxGenius
Date: 28 Jun 2020 02:28:28
Computational Geometry: Algorithms and Applications

Computational Geometry: Algorithms and Applications by Mark de Berg
English | PDF | 1997 | 367 Pages | ISBN : 3662034298 | 40.47 MB

This all-new introduction to computational geometry is a textbook for high-level undergraduate and low-level graduate courses. The focus is on algorithms and hence the book is well suited for students in computer science and engineering. Motivation is provided from the application areas – all solutions and techniques from computational geometry are related to particular applications in robotics, graphics, CAD/CAM, and geographic information systems.
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Nonlinear Computational Geometry (Repost)

Posted By: AvaxGenius
Date: 31 May 2020 22:41:27
Nonlinear Computational Geometry (Repost)

Nonlinear Computational Geometry by Ioannis Z. Emiris
English | PDF | 2010 | 243 Pages | ISBN : 1441909982 | 7.89 MB

An original motivation for algebraic geometry was to understand curves and surfaces in three dimensions. Recent theoretical and technological advances in areas such as robotics, computer vision, computer-aided geometric design and molecular biology, together with the increased availability of computational resources, have brought these original questions once more into the forefront of research.
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Triangulations: Structures for Algorithms and Applications (Repost)

Posted By: AvaxGenius
Date: 15 Mar 2020 21:11:57
Triangulations: Structures for Algorithms and Applications (Repost)

Triangulations: Structures for Algorithms and Applications By Jesús A. De Loera
English | PDF | 2010 | 547 Pages | ISBN : 3642129706 | 17.45 MB

Triangulations appear everywhere, from volume computations and meshing to algebra and topology. This book studies the subdivisions and triangulations of polyhedral regions and point sets and presents the first comprehensive treatment of the theory of secondary polytopes and related topics.
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Algorithmic Randomness and Complexity

Posted By: AvaxGenius
Date: 15 Mar 2020 21:02:59
Algorithmic Randomness and Complexity

Algorithmic Randomness and Complexity by Rodney G. Downey
English | PDF | 2010 | 883 Pages | ISBN : 0387955674 | 8.55 MB

Intuitively, a sequence such as 101010101010101010… does not seem random, whereas 101101011101010100…, obtained using coin tosses, does. How can we reconcile this intuition with the fact that both are statistically equally likely? What does it mean to say that an individual mathematical object such as a real number is random, or to say that one real is more random than another? And what is the relationship between randomness and computational power. The theory of algorithmic randomness uses tools from computability theory and algorithmic information theory to address questions such as these.
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