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High-Dimensional Optimization and Probability (eBook)

With a View Towards Data Science
Artikelnummer: 978-3-031-00832-0
Einband: PDF
Verfügbarkeit: Download, sofort verfügbar (Link per E-Mail)
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This volume presents extensive research devoted to a broad spectrum of mathematics with emphasis on interdisciplinary aspects of Optimization and Probability. Chapters also emphasize applications to Data Science, a timely field with a high impact in our modern society. The discussion presents modern, state-of-the-art, research results and advances in areas including non-convex optimization, decentralized distributed convex optimization, topics on surrogate-based reduced dimension global optimization in process systems engineering, the projection of a point onto a convex set, optimal sampling for learning sparse approximations in high dimensions, the split feasibility problem, higher order embeddings, codifferentials and quasidifferentials of the expectation of nonsmooth random integrands, adjoint circuit chains associated with a random walk, analysis of the trade-off between sample size and precision in truncated ordinary least squares, spatial deep learning, efficient location-based tracking for IoT devices using compressive sensing and machine learning techniques, and nonsmooth mathematical programs with vanishing constraints in Banach spaces.

The book is a valuable source for graduate students as well as researchers working on Optimization, Probability and their various interconnections with a variety of other areas.

Chapter 12 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.



This volume presents extensive research devoted to a broad spectrum of mathematics with emphasis on interdisciplinary aspects of Optimization and Probability. Chapters also emphasize applications to Data Science, a timely field with a high impact in our modern society. The discussion presents modern, state-of-the-art, research results and advances in areas including non-convex optimization, decentralized distributed convex optimization, topics on surrogate-based reduced dimension global optimization in process systems engineering, the projection of a point onto a convex set, optimal sampling for learning sparse approximations in high dimensions, the split feasibility problem, higher order embeddings, codifferentials and quasidifferentials of the expectation of nonsmooth random integrands, adjoint circuit chains associated with a random walk, analysis of the trade-off between sample size and precision in truncated ordinary least squares, spatial deep learning, efficient location-based tracking for IoT devices using compressive sensing and machine learning techniques, and nonsmooth mathematical programs with vanishing constraints in Banach spaces.

The book is a valuable source for graduate students as well as researchers working on Optimization, Probability and their various interconnections with a variety of other areas.

Chapter 12 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.



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VerlagSpringer International Publishing
EinbandPDF
Erscheinungsjahr2022
Seitenangabe417 S.
AusgabekennzeichenEnglisch
AbbildungenVIII, 417 p. 40 illus., 33 illus. in color.
Masse9'965 KB
PlattformPDF
ReiheSpringer Optimization and Its Applications; Mathematics and Statistics; Mathematics and Statistics
AutorNikeghbali, Ashkan (Hrsg.) / Pardalos, Panos M. (Hrsg.) / Raigorodskii, Andrei M. (Hrsg.) / Rassias, Michael Th. (Hrsg.)

Alle Bände der Reihe "Springer Optimization and Its Applications; Mathematics and Statistics; Mathematics and Statistics (R0)"

Über den Autor Ashkan (Hrsg.) Nikeghbali

Ashkan Nikeghbali holds the Chair in Financial Mathematics at the University of Zürich. His fields of research span through a broad spectrum of areas, including finance mathematics, number theory, random matrices, and stochastic processes. Since 2016 Professor Nikeghbali has been a member of the Scientific Advisory Board of swissQuant and a member of the Advisory Board of EVMTech. He is also a strategy advisor for data analysis and modeling of stochastic processes at Roche Holding in Basel, Switzerland. Professor Nikeghbali has authored/edited several books. In 2019, he was awarded with an Honorary Doctorate from the "1 Decembrie 1918" University of Alba Iulia. Panos Pardalos is a Distinguished Emeritus Professor in the Department of Industrial and Systems Engineering at the University of Florida, and an affiliated faculty of Biomedical Engineering and Computer Science & Information & Engineering departments. He is a world-renowned leader in Global Optimization, Mathematical Modeling, Energy Systems, Financial applications, and Data Sciences. He is a Fellow of AAAS, AAIA, AIMBE, EUROPT, and INFORMS and was awarded the 2013 Constantin Caratheodory Prize of the International Society of Global Optimization. In addition, Panos Pardalos has been awarded the 2013 EURO Gold Medal prize bestowed by the Association for European Operational Research Societies. This medal is the preeminent European award given to Operations Research (OR) professionals for "scientific contributions that stand the test of time." He has also been awarded the prestigious Humboldt Research Award (2018-2019). The Humboldt Research Award is granted in recognition of a researcher's entire achievements to date - fundamental discoveries, new theories, insights that have had significant impact on their discipline. Michael Th. Rassias is an Associate Professor at the Department of Mathematics and Engineering Sciences of the Hellenic Military Academy. During the academic year 2014-2015, he was a Postdoctoral researcher at the Department of Mathematics of Princeton University and the Department of Mathematics of ETH-Zürich, conducting research at Princeton. While at Princeton, he prepared with John F Nash, Jr. (Nobel Prize, 1994 and Abel Prize, 2015) the volume Open Problems in Mathematics, Springer, 2016. He has received several awards in mathematical problem-solving competitions, including a Silver medal at the International Mathematical Olympiad of 2003 in Tokyo. He has authored and edited several books, including the edited volume Analysis at Large jointly with A. Avila (Fields Medal, 2014) and Y. Sinai (Abel Prize, 2014). His current research interests lie in mathematical analysis, analytic number theory, and more specifically the Riemann Hypothesis, Goldbach's conjecture, the distribution of prime numbers, approximation theory, functional equations, analytic inequalities and Cryptography.

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