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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
High-Dimensional Optimization and Probability: With a View Towards Data Science

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

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

Paperback

Series: Springer Optimization and Its Applications, Book 191

Business GeneralGeneral MathematicsProbability & Statistics

ISBN10: 3031008340
ISBN13: 9783031008344
Publisher: Springer Nature
Published: Aug 5 2023
Pages: 417
Weight: 1.31
Height: 0.87 Width: 6.14 Depth: 9.21
Language: English
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.

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