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High-Dimensional Optimization and Probability

With a View Towards Data Science

Produktform: Buch / Einband - fest (Hardcover)

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. weiterlesen

Dieser Artikel gehört zu den folgenden Serien

Sprache(n): Englisch

ISBN: 978-3-031-00831-3 / 978-3031008313 / 9783031008313

Verlag: Springer International Publishing

Erscheinungsdatum: 05.08.2022

Seiten: 417

Auflage: 1

Herausgegeben von Panos M. Pardalos, Michael Th. Rassias, Ashkan Nikeghbali, Andrei M. Raigorodskii

90,94 € inkl. MwSt.
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