High-Dimensional Optimization and Probability
Editor
- Ashkan Nikeghbali(Universität Zürich)Panos M. Pardalos(University of Florida)Andrei M. Raigorodskii(Moscow Institute of Physics & Technology)Michael Th. Rassias(Hellenic Military Academy)
Abstract
No abstract is available for this item.Individual chapters are listed in the "Chapters" tab
Suggested Citation
- Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), 2022. "High-Dimensional Optimization and Probability," Springer Optimization and Its Applications, Springer, number 978-3-031-00832-0, June.
Handle: RePEc:spr:spopap:978-3-031-00832-0
DOI: 10.1007/978-3-031-00832-0Download full text from publisher
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The following chapters of this book are listed in IDEAS- Majid E. Abbasov, 2022. "Projection of a Point onto a Convex Set via Charged Balls Method," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 1-8, Springer.
- Ben Adcock & Juan M. Cardenas & Nick Dexter & Sebastian Moraga, 2022. "Towards Optimal Sampling for Learning Sparse Approximations in High Dimensions," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 9-77, Springer.
- Marina Danilova & Pavel Dvurechensky & Alexander Gasnikov & Eduard Gorbunov & Sergey Guminov & Dmitry Kamzolov & Innokentiy Shibaev, 2022. "Recent Theoretical Advances in Non-Convex Optimization," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 79-163, Springer.
- Shusen Ding & Guannan Shi & Donna Sylvester, 2022. "Higher Order Embeddings for the Composition of the Harmonic Projection and Homotopy Operators," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 165-183, Springer.
- M. V. Dolgopolik, 2022. "Codifferentials and Quasidifferentials of the Expectation of Nonsmooth Random Integrands and Two-Stage Stochastic Programming," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 185-218, Springer.
- Chrysoula Ganatsiou, 2022. "On the Expected Extinction Time for the Adjoint Circuit Chains Associated with a Random Walk with Jumps in Random Environments," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 219-239, Springer.
- Giorgio Gnecco & Fabio Raciti & Daniela Selvi, 2022. "A Statistical Learning Theory Approach for the Analysis of the Trade-off Between Sample Size and Precision in Truncated Ordinary Least Squares," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 241-252, Springer.
- Eduard Gorbunov & Alexander Rogozin & Aleksandr Beznosikov & Darina Dvinskikh & Alexander Gasnikov, 2022. "Recent Theoretical Advances in Decentralized Distributed Convex Optimization," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 253-325, Springer.
- Eligius M. T. Hendrix & Mercedes Paoletti & Juan Mario Haut, 2022. "On Training Set Selection in Spatial Deep Learning," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 327-339, Springer.
- Kody Kazda & Xiang Li, 2022. "Surrogate-Based Reduced-Dimension Global Optimization in Process Systems Engineering," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 341-357, Springer.
- Lulu Liu & Qiao-Li Dong & Shen Wang & Michael Th. Rassias, 2022. "A Viscosity Iterative Method with Alternated Inertial Terms for Solving the Split Feasibility Problem," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 359-372, Springer.
- Ramy Aboushelbaya & Taimir Aguacil & Qiuting Huang & Peter A. Norreys, 2022. "Efficient Location-Based Tracking for IoT Devices Using Compressive Sensing and Machine Learning Techniques," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 373-393, Springer.
- Vivek Laha & Vinay Singh & Yogendra Pandey & S. K. Mishra, 2022. "Nonsmooth Mathematical Programs with Vanishing Constraints in Banach Spaces," Springer Optimization and Its Applications, in: Ashkan Nikeghbali & Panos M. Pardalos & Andrei M. Raigorodskii & Michael Th. Rassias (ed.), High-Dimensional Optimization and Probability, pages 395-417, Springer.
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