High-Dimensional Optimization and Probability

With a View Towards Data Science

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Springer


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Springer Optimization and Its Applications

Paru le : 2022-08-04



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Description
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.


Pages
417 pages
Collection
Springer Optimization and Its Applications
Parution
2022-08-04
Marque
Springer
EAN papier
9783031008313
EAN PDF
9783031008320

Informations sur l'ebook
Nombre pages copiables
4
Nombre pages imprimables
41
Taille du fichier
9964 Ko
Prix
89,66 €
EAN EPUB
9783031008320

Informations sur l'ebook
Nombre pages copiables
4
Nombre pages imprimables
41
Taille du fichier
44780 Ko
Prix
89,66 €