High-Dimensional Optimization and Probability: With a View Towards Data Science

This volume presents research on Optimization and Probability with interdisciplinary applications to Data Science. It covers non-convex optimization, decentralized distributed convex optimization, surrogate-based reduced dimension global optimization, optimal sampling, split feasibility problem, higher order embeddings, codifferentials, quasidifferentials, adjoint circuit chains, spatial deep learning, efficient location-based tracking, and nonsmooth mathematical programs with vanishing constraints. The book is a valuable resource for graduate students and researchers.

Format: Hardback
Length: 417 pages
Publication date: 05 August 2022
Publisher: Springer International Publishing AG

This comprehensive volume delves into extensive research across a diverse range of mathematics, with a strong emphasis on interdisciplinary aspects of Optimization and Probability. The chapters also highlight the timely applications of these fields to Data Science, a field with significant impact in our modern society. The discussion presents cutting-edge, state-of-the-art research results and advancements in various areas, including non-convex optimization, decentralized distributed convex optimization, surrogate-based reduced dimension global optimization in process systems engineering, projection of a point onto a convex set, optimal sampling for learning sparse approximations in high dimensions, 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 serves as a valuable resource for graduate students and researchers engaged in Optimization, Probability, and their interplay with diverse other fields. Chapter 12 is freely accessible open access under a Creative Commons Attribution 4.0 International License via the link.springer.com.

Weight: 805g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031008313
Edition number: 1st ed. 2022