Open Problems In Optimization And Data Analysis

Author : Panos M. Pardalos
Publisher : Springer
ISBN 13 : 3319991426
Total Pages : 330 pages
Book Rating : 4.29/5 ( download)

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Download or read book Open Problems in Optimization and Data Analysis written by Panos M. Pardalos and published by Springer. This book was released on 2018-12-04 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book. Each contribution provides the fundamentals needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline. The contributions contained in this book are based on lectures focused on “Challenges and Open Problems in Optimization and Data Science” presented at the Deucalion Summer Institute for Advanced Studies in Optimization, Mathematics, and Data Science in August 2016.

Author : Stephen J. Wright
Publisher : Cambridge University Press
ISBN 13 : 1316518981
Total Pages : 239 pages
Book Rating : 4.84/5 ( download)

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Download or read book Optimization for Data Analysis written by Stephen J. Wright and published by Cambridge University Press. This book was released on 2022-04-21 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.

Author : Boris Goldengorin
Publisher : Springer
ISBN 13 : 3319420569
Total Pages : 507 pages
Book Rating : 4.61/5 ( download)

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Download or read book Optimization and Its Applications in Control and Data Sciences written by Boris Goldengorin and published by Springer. This book was released on 2016-09-29 with total page 507 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on recent research in modern optimization and its implications in control and data analysis. This book is a collection of papers from the conference “Optimization and Its Applications in Control and Data Science” dedicated to Professor Boris T. Polyak, which was held in Moscow, Russia on May 13-15, 2015. This book reflects developments in theory and applications rooted by Professor Polyak’s fundamental contributions to constrained and unconstrained optimization, differentiable and nonsmooth functions, control theory and approximation. Each paper focuses on techniques for solving complex optimization problems in different application areas and recent developments in optimization theory and methods. Open problems in optimization, game theory and control theory are included in this collection which will interest engineers and researchers working with efficient algorithms and software for solving optimization problems in market and data analysis. Theoreticians in operations research, applied mathematics, algorithm design, artificial intelligence, machine learning, and software engineering will find this book useful and graduate students will find the state-of-the-art research valuable.

Author : Ilya Bychkov
Publisher : Springer Nature
ISBN 13 : 3030371573
Total Pages : 251 pages
Book Rating : 4.79/5 ( download)

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Download or read book Network Algorithms, Data Mining, and Applications written by Ilya Bychkov and published by Springer Nature. This book was released on 2020-02-22 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings presents the result of the 8th International Conference in Network Analysis, held at the Higher School of Economics, Moscow, in May 2018. The conference brought together scientists, engineers, and researchers from academia, industry, and government. Contributions in this book focus on the development of network algorithms for data mining and its applications. Researchers and students in mathematics, economics, statistics, computer science, and engineering find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks. Machine learning techniques in network settings including community detection, clustering, and biclustering algorithms are presented with applications to social network analysis.

Author : Miroslav Fiedler
Publisher : Springer Science & Business Media
ISBN 13 : 0387326987
Total Pages : 222 pages
Book Rating : 4.86/5 ( download)

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Download or read book Linear Optimization Problems with Inexact Data written by Miroslav Fiedler and published by Springer Science & Business Media. This book was released on 2006-07-18 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear programming has attracted the interest of mathematicians since World War II when the first computers were constructed. Early attempts to apply linear programming methods practical problems failed, in part because of the inexactness of the data used to create the models. This book presents a comprehensive treatment of linear optimization with inexact data, summarizing existing results and presenting new ones within a unifying framework.

Author : Panos M. Pardalos
Publisher : Springer Nature
ISBN 13 : 3030665151
Total Pages : 388 pages
Book Rating : 4.59/5 ( download)

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Download or read book Black Box Optimization, Machine Learning, and No-Free Lunch Theorems written by Panos M. Pardalos and published by Springer Nature. This book was released on 2021-05-27 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem.

Author : M. R. Osborne
Publisher :
ISBN 13 :
Total Pages : 408 pages
Book Rating : 4.26/5 ( download)

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Download or read book Finite Algorithms in Optimization and Data Analysis written by M. R. Osborne and published by . This book was released on 1985-12-23 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significance and originality of this book derive from its novel approach to those optimization problems in which an active set strategy leads to a finite algorithm, such as linear and quadratic programming or l1 and l approximations.

Author : Dimitris Souravlias
Publisher : Springer Nature
ISBN 13 : 3030685144
Total Pages : 92 pages
Book Rating : 4.40/5 ( download)

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Download or read book Algorithm Portfolios written by Dimitris Souravlias and published by Springer Nature. This book was released on 2021-03-24 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers algorithm portfolios, multi-method schemes that harness optimization algorithms into a joint framework to solve optimization problems. It is expected to be a primary reference point for researchers and doctoral students in relevant domains that seek a quick exposure to the field. The presentation focuses primarily on the applicability of the methods and the non-expert reader will find this book useful for starting designing and implementing algorithm portfolios. The book familiarizes the reader with algorithm portfolios through current advances, applications, and open problems. Fundamental issues in building effective and efficient algorithm portfolios such as selection of constituent algorithms, allocation of computational resources, interaction between algorithms and parallelism vs. sequential implementations are discussed. Several new applications are analyzed and insights on the underlying algorithmic designs are provided. Future directions, new challenges, and open problems in the design of algorithm portfolios and applications are explored to further motivate research in this field.

Author : Sebastià Xambó-Descamps
Publisher : Springer Nature
ISBN 13 : 3030744868
Total Pages : 179 pages
Book Rating : 4.61/5 ( download)

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Download or read book Systems, Patterns and Data Engineering with Geometric Calculi written by Sebastià Xambó-Descamps and published by Springer Nature. This book was released on 2021-07-16 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: The intention of this collection agrees with the purposes of the homonymous mini-symposium (MS) at ICIAM-2019, which were to overview the essentials of geometric calculus (GC) formalism, to report on state-of-the-art applications showcasing its advantages and to explore the bearing of GC in novel approaches to deep learning. The first three contributions, which correspond to lectures at the MS, offer perspectives on recent advances in the application GC in the areas of robotics, molecular geometry, and medical imaging. The next three, especially invited, hone the expressiveness of GC in orientation measurements under different metrics, the treatment of contact elements, and the investigation of efficient computational methodologies. The last two, which also correspond to lectures at the MS, deal with two aspects of deep learning: a presentation of a concrete quaternionic convolutional neural network layer for image classification that features contrast invariance and a general overview of automatic learning aimed at steering the development of neural networks whose units process elements of a suitable algebra, such as a geometric algebra. The book fits, broadly speaking, within the realm of mathematical engineering, and consequently, it is intended for a wide spectrum of research profiles. In particular, it should bring inspiration and guidance to those looking for materials and problems that bridge GC with applications of great current interest, including the auspicious field of GC-based deep neural networks.

Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 0309287782
Total Pages : 191 pages
Book Rating : 4.84/5 ( download)

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Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-10-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.