Robustness of Network Structure Identification
In this chapter, we discuss robustness of network structure identification algorithms. We understand robustness of identification algorithm as the stability of the risk function with respect to the distribution of the vector X from some class of distribut
- PDF / 1,421,836 Bytes
- 102 Pages / 439.42 x 666.14 pts Page_size
- 16 Downloads / 235 Views
V. A. Kalyagin A. P. Koldanov P. A. Koldanov P. M. Pardalos
Statistical Analysis of Graph Structures in Random Variable Networks
SpringerBriefs in Optimization Series Editors Sergiy Butenko, Texas A & M University, College Station, TX, USA Mirjam Dür, University of Trier, Trier, Germany Panos M. Pardalos, University of Florida, Gainesville, FL, USA János D. Pintér, Lehigh University, Bethlehem, PA, USA Stephen M. Robinson, University of Wisconsin-Madison, Madison, WI, USA Tamás Terlaky, Lehigh University, Bethlehem, PA, USA My T. Thai , University of Florida, Gainesville, FL, USA
SpringerBriefs present concise summaries of cutting-edge research and practical applications across a wide spectrum of fields. Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic. Briefs are characterized by fast, global electronic dissemination, standard publishing contracts, standardized manuscript preparation and formatting guidelines, and expedited production schedules. Typical topics might include: • A timely report of state-of-the art techniques • A bridge between new research results, as published in journal articles, and a contextual literature review • A snapshot of a hot or emerging topic • An in-depth case study • A presentation of core concepts that students must understand in order to make independent contributions SpringerBriefs in Optimization showcase algorithmic and theoretical techniques, case studies, and applications within the broad-based field of optimization. Manuscripts related to the ever-growing applications of optimization in applied mathematics, engineering, medicine, economics, and other applied sciences are encouraged. Titles from this series are indexed by Web of Science, Mathematical Reviews, and zbMATH.
More information about this series at http://www.springer.com/series/8918
V. A. Kalyagin • A. P. Koldanov P. A. Koldanov • P. M. Pardalos
Statistical Analysis of Graph Structures in Random Variable Networks
123
V. A. Kalyagin Laboratory of Algorithms and Technologies for Networks Analysis National Research University Higher School of Economics Nizhny Novgorod, Russia
A. P. Koldanov Laboratory of Algorithms and Technologies for Networks Analysis National Research University Higher School of Economics Nizhny Novgorod, Russia
P. A. Koldanov Laboratory of Algorithms and Technologies for Networks Analysis National Research University Higher School of Economics Nizhny Novgorod, Russia
P. M. Pardalos Department of Industrial & Systems Engineering University of Florida Gainesville, FL, USA
ISSN 2190-8354 ISSN 2191-575X (electronic) SpringerBriefs in Optimization ISBN 978-3-030-60292-5 ISBN 978-3-030-60293-2 (eBook) https://doi.org/10.1007/978-3-030-60293-2 © The Author(s) 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in a
Data Loading...
