Dr Alan J Hoffman is a pioneer in linear programming, combinatorial optimization, and the study of graph spectra. In his principal research interests, which include the fields of linear inequalities, combinatorics, and matrix theory, he and his collaborators have contributed fundamental concepts and theorems, many of which bear their names.This volume of Dr Hoffman's selected papers is divided into seven sections: geometry; combinatorics; matrix inequalities and eigenvalues; linear inequalities and linear programming; combinatorial optimization; greedy algorithms; graph spectra. Dr Hoffman has supplied background commentary and anecdotal remarks for each of the selected papers. He has also provided autobiographical notes showing how he chose mathematics as his profession, and the influences and motivations which shaped his career.

Dr Alan J Hoffman is a pioneer in linear programming, combinatorial optimization, and the study of graph spectra. In his principal research interests, which include the fields of linear inequalities, combinatorics, and matrix theory, he and his collaborators have contributed fundamental concepts and theorems, many of which bear their names. This volume of Dr Hoffman's selected papers is divided into seven sections: geometry; combinatorics; matrix inequalities and eigenvalues; linear inequalities and linear programming; combinatorial optimization; greedy algorithms; graph spectra. Dr Hoffman has supplied background commentary and anecdotal remarks for each of the selected papers. He has also provided autobiographical notes showing how he chose mathematics as his profession, and the influences and motivations which shaped his career. Contents: The Variation of the Spectrum of a Normal Matrix (with H W Wielandt); Integral Boundary Points of Convex Polyhedra (with J Kruskal); On Moore Graphs with Diameters 2 and 3 (with R R Singleton); Cycling in the Simplex Algorithm; On Approximate Solutions of Systems of Linear Inequalities; On the Polynomial of a Graph; Some Recent Applications of the Theory of Linear Inequalities of Extremal Combinatorial Analysis; On Simple Linear Programming Problems; Self-Orthogonal Latin Squares (with R K Brayton & D Coppersmith); On the Nonsingularity of Complex Matrices (with P Camion); A Generalization of Max Flow-Min Cut; A Characterization of Comparability Graphs and of Interval Graphs (with P C Gilmore); and 33 other papers. Readership: Researchers in linear programming and inequalities, combinatorics, combinatorial optimization, graph theory, matrix theory and operations research.

Dr. Alan J Hoffman is a pioneer in linear programming, combinatorial optimization, and the study of graph spectra. In his principal research interests, which include the fields of linear inequalities, combinatorics, and matrix theory, he and his collaboratorics, and matrix theory, he and his collaborators have contributed fundamental concepts and theorems, amany of which bear their names. This volume of Dr. Hoffman's selected papers is divided into seven sections: geometry; combinatorics; matrix inequalities and eigenvalues; linear inequalities and linear programming; combinatorial optimization; greedy algorithms; graph spectra. Dr. Hoffman has supplied background commentary and anecdotal remarks for each of the selected papers. He has also provided autobiogrphical notes showing how he chose mathematics as his profession, and the influences and motivations which shaped his career. Contents: The Variation of the Spectrum of a Normal Matrix (with H W Wielandt); Integral Boundary Points of ConvexPolyhedra (with J Kruskal); On Moore Graphs with Diameters 2 and 3 (with R Singleton); Cycling in the Simplex Algorithm; On Approximate Solutions of Systems of Linear Inequalities; On the Polynomial of a Graph; Some Recent Applications of the Theory of Linear Inequalities of Extrenal Combinatorial Analysis; and 37 other papers. Readership: Researchers in linear programming and inequalities, combinatorics, combinatorial optimization, graph theory, matrix theory and operations research.

This two-volume set (LNCS 13885-13886) constitutes the refereed proceedings of the 23rd Scandinavian Conference on Image Analysis, SCIA 2023, held in Lapland, Finland, in April 2023. The 67 revised papers presented were carefully reviewed and selected from 108 submissions. The contributions are structured in topical sections on datasets and evaluation; action and behaviour recognition; image and video processing, analysis, and understanding; detection, recognition, classification, and localization in 2D and/or 3D; machine learning and deep learning; segmentation, grouping, and shape; vision for robotics and autonomous vehicles; biometrics, faces, body gestures and pose; 3D vision from multiview and other sensors; vision applications and systems.

This book constitutes the refereed proceedings of the 19th International Conference on CParallel and Distributed Computing, Applications and Technologies, PDCAT 2018, held in Jeju Island, South Korea, in August 2018. The 35 revised full papers presented along with the 14 short papers and were carefully reviewed and selected from 150 submissions. The papers of this volume are organized in topical sections on wired and wireless communication systems, high dimensional data representation and processing, networks and information security, computing techniques for efficient networks design, electronic circuits for communication systems.

This book provides a guide to the analysis of complex systems through the lens of data science.

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

Seeks to improve communication between managers and professionals in OR/MS.

Harry M Markowitz received the Nobel Prize in Economics in 1990 for his pioneering work in portfolio theory. He also received the von Neumann Prize from the Institute of Management Science and the Operations Research Institute of America in 1989 for his work in portfolio theory, sparse matrices and the SIMSCRIPT computer language. While Dr Markowitz is well-known for his work on portfolio theory, his work on sparse matrices remains an essential part of linear optimization calculations. In addition, he designed and developed SIMSCRIPT — a computer programming language. SIMSCRIPT has been widely used for simulations of systems such as air transportation and communication networks.This book consists of a collection of Dr Markowitz's most important works in these and other fields.

"Contains numerous simple examples and illustrative diagrams....For anyone seeking information about eigenvalue inclusion theorems, this book will be a great reference." --Mathematical Reviews This book studies the original results, and their extensions, of the Russian mathematician S.A. Geršgorin who wrote a seminal paper in 1931 on how to easily obtain estimates of all n eigenvalues (characteristic values) of any given n-by-n complex matrix.