Name: High-Dimensional Optimization and Probability: With a View Towards Data Science (en Inglés)
Price: 96.45 USD
Availability: InStock
Author: Nikeghbali, Ashkan ; Pardalos, Panos M. ; Raigorodskii, Andrei M.
ISBN: 9783031008313

portada High-Dimensional Optimization and Probability: With a View Towards Data Science (en Inglés)

High-Dimensional Optimization and Probability: With a View Towards Data Science (en Inglés)

Formato

Libro Físico

Ilustrado por

Panos M. Pardalos

Ashkan Nikeghbali

Andrei M. Raigorodskii

Editorial

Springer

Autor

Nikeghbali, Ashkan ; Pardalos, Panos M. ; Raigorodskii, Andrei M.

Idioma

Inglés

N° páginas

417

Encuadernación

Tapa Dura

Dimensiones

23.4 x 15.6 x 2.4 cm

Peso

0.77 kg.

ISBN13

9783031008313

Categorías

Gestión del conocimiento
Probabilidad y estadística
Matemáticas aplicadas

High-Dimensional Optimization and Probability: With a View Towards Data Science (en Inglés)

Panos M. Pardalos (Ilustrado por) · Ashkan Nikeghbali (Ilustrado por) · Andrei M. Raigorodskii (Ilustrado por) · Springer · Tapa Dura

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Reseña del libro "High-Dimensional Optimization and Probability: With a View Towards Data Science (en Inglés)"

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.