Tag: Optimal

Topological Optimization and Optimal Transport In the Applied Sciences


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English | 2017 | ISBN: 3110439263 | 511 Pages | EPUB (True) | 17 MB
By discussing topics such as shape representations, relaxation theory and optimal transport, trends and synergies of mathematical tools required for optimization of geometry and topology of shapes are explored. Furthermore, applications in science and engineering, including economics, social sciences, biology, physics and image processing are covered.

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Optimal transport, old and new


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2009 | 997 Pages | ISBN: 3540710493 | PDF | 5 MB
At the close of the 1980s, the independent contributions of Yann Brenier, Mike Cullen and John Mather launched a revolution in the venerable field of optimal transport founded by G. Monge in the 18th century, which has made breathtaking forays into various other domains of mathematics ever since. The author presents a broad overview of this area, supplying complete and self-contained proofs of all the fundamental results of the theory of optimal transport at the appropriate level of generality. Thus, the book encompasses the broad spectrum ranging from basic theory to the most recent research results. PhD students or researchers can read the entire book without any prior knowledge of the field. A comprehensive bibliography with notes that extensively discuss the existing literature underlines the book’s value as a most welcome reference text on this subject.

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M-statistics Optimal Statistical Inference for a Small Sample


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English | August 22, 2023 | ISBN: 1119891795 | 240 pages | MOBI | 17 Mb
M-STATISTICS A comprehensive resource providing new statistical methodologies and demonstrating how new approaches work for applications M-statistics introduces a new approach to statistical inference, redesigning the fundamentals of statistics, and improving on the classical methods we already use. This book targets exact optimal statistical inference for a small sample under one methodological umbrella. Two competing approaches are maximum concentration (MC) and mode (MO) statistics combined under one methodological umbrella, which is why the symbolic equation M=MC+MO. M-statistics defines an estimator as the limit point of the MC or MO exact optimal confidence interval when the confidence level approaches zero, the MC and MO estimator, respectively. Neither mean nor variance plays a role in M-statistics theory. Novel statistical methodologies in the form of double-sided unbiased and short confidence intervals and tests apply to major statistical Our new developments are accompanied by respective algorithms and R codes, available at GitHub, and as such readily available for applications. M-statistics is suitable for professionals and students alike. It is highly useful for theoretical statisticians and teachers, researchers, and data science analysts as an alternative to classical and approximate statistical inference.

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Advances in Metaheuristic Algorithms for Optimal Design of Structures, Third Edition (2024)


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English | EPUB | 2021 | 890 Pages | ISBN : 3030593916 | 163.5 MB
This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his graduate students, consisting of Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Democratic Particle Swarm Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization.

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Chaotic Meta-heuristic Algorithms for Optimal Design of Structures


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English | 2024 | ISBN: 3031489179 | 454 Pages | PDF EPUB (True) | 91 MB
In this book, various chaos maps are embedded in eleven efficient and well-known metaheuristics and a significant improvement in the optimization results is achieved. The two basic steps of metaheuristic algorithms consist of exploration and exploitation. The imbalance between these stages causes serious problems for metaheuristic algorithms, which are immature convergence and stopping in local optima. Chaos maps with chaotic jumps can save algorithms from being trapped in local optima and lead to convergence toward global optima. Embedding these maps in the exploration phase, exploitation phase, or both simultaneously corresponds to three efficient and useful scenarios. By creating competition between different modes and increasing diversity in the search space and creating sudden jumps in the search phase, improvements are achieved for chaotic algorithms. Four Chaotic Algorithms, including Chaotic Cyclical Parthenogenesis Algorithm, Chaotic Water Evaporation Optimization, Chaotic Tug-of-War Optimization, and Chaotic Thermal Exchange Optimization are developed.

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Optimal Spending on Cybersecurity Measures Risk Management


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English | 2021 | ISBN: 1032061405 | 115 Pages | PDF (True) | 3.4 MB
Based on unique and distinct research completed within the field of risk-management and information security, this book provides insight into organizational risk-management processes utilized in determining cybersecurity investments. It describes how theoretical models and frameworks rely on either specific scenarios or controlled conditions and how decisions on cybersecurity spending within organizations-specifically, the funding available in comparison to the recommended security measures necessary for compliance-vary depending on stakeholders. As the trade-off between the costs of implementing a security measure and the benefit derived from the implementation of security controls is not easily measured, a business leader’s decision to fund security measures may be biased. The author presents an innovative approach to assess cybersecurity initiatives with a risk-management perspective and leverages a data-centric focus on the evolution of cyber-attacks.

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