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Methodology of Complex Activity: Foundations of Understanding and Modelling (Studies in Systems, Decision and Control #300)
by Dmitry A. Novikov Mikhail V. BelovThis book develops and describes a general methodology that can be applied to any complex human activity (activity with a non-trivial, multi-level internal structure).The structural components of complex activities are considered, and their logical, cause-and-effect, and process structures are functionally described. Considerable attention is paid to organization and management, uncertainties, and the lifecycles of activities, as well as the actors, subject matter, resources, knowledge, and methods involved. Several typical examples are used throughout the text to illustrate the implementation of common approaches involving the functioning of work groups, organizational units, projects, and organizations in general: a retail bank, an aircraft manufacturer, a fire department, and a nuclear power plant. In addition, the book employs a system of connected technical models, in order to ensure that the results are of practical applicability for both experts on the ground and scholars engaged in research on the general principles of how activities (practical, scientific, etc.) are organized or on the management of socio-technical systems.
Methodology to Improve Control Plane Security in SDN Environments
by Wendwossen Desalegn Javed Shaikh Bayisa TayeThis book unveils a blueprint for safeguarding the very backbone of modern communication networks. It offers a roadmap towards fortifying SDN infrastructures against the relentless onslaught of cyber threats, ensuring resilience and reliability in an ever-evolving digital landscape.This is an exhaustive study of crafting a robust security solution tailored for the SDN environment, specifically targeting the detection and mitigation of distributed denial of service (DDoS) attacks on the control plane. The methodology hinges on an early detection strategy, meticulously aligned with industry standards, serving as a beacon for professionals navigating the intricate realm of implementing security solutions. This reference elucidates an innovative approach devised to identify and mitigate the inherent risks associated with the OpenFlow protocol and its POX controller. Validated through rigorous simulations conducted within controlled environments utilizing the Mininet tool and SDN controller, the methodology unfolds, showcasing the intricate dance between theory and practice.Through meticulous observation of detection algorithm results in simulated environments, followed by real-world implementation within network testbeds, the proposed solution emerges triumphant. Leveraging network entropy calculation, coupled with swift port blocking mechanisms, the methodology stands as a formidable barrier against a DDoS attack such as TCP, UDP, and ICMP floods.
Methods and Applications for Modeling and Simulation of Complex Systems: 18th Asia Simulation Conference, AsiaSim 2018, Kyoto, Japan, October 27–29, 2018, Proceedings (Communications in Computer and Information Science #946)
by Liang Li Satoshi Tanaka Kyoko HasegawaThis volume constitutes the proceedings of the 18th Asia Simulation Conference, AsiaSim 2018, held in Kyoto, Japan, in August 2018.The 45 revised full papers presented in this volume were carefully reviewed and selected from 90 submissions. The papers are organized in topical sections on modeling and simulation technology; soft computing and machine learning; high performance computing and cloud computing; simulation technology for industry; simulation technology for intelligent society; simulation of instrumentation and control application; computational mathematics and computational science; flow simulation; visualization and computer vision to support simulation.
Methods and Applications for Modeling and Simulation of Complex Systems: 19th Asia Simulation Conference, AsiaSim 2019, Singapore, October 30 – November 1, 2019, Proceedings (Communications in Computer and Information Science #1094)
by Gary Tan Axel Lehmann Yong Meng Teo Wentong CaiThis volume constitutes the proceedings of the 19th Asia Simulation Conference, AsiaSim 2019, held in Singapore, Singapore, in October 2019.The 19 revised full papers and 5 short papers presented in this volume were carefully reviewed and selected from 36 submissions. The papers are organized in topical sections on simulation and modeling methodology; numerical and Monte Carlo simulation; simulation applications: blockchain, deep learning and cloud; simulation and visualization; simulation applications; short papers.
Methods and Applications for Modeling and Simulation of Complex Systems: 20th Asian Simulation Conference, AsiaSim 2021, Virtual Event, November 17–20, 2021, Proceedings (Communications in Computer and Information Science #1636)
by Byeong-Yun Chang Changbeom ChoiThis volume constitutes the proceedings of the 20th Asian Simulation Conference, AsiaSim 2021, held as a virtual event in November 2021.The 9 full papers presented in this volume were carefully reviewed and selected from 23 submissions. The papers are organized in topical sections on simulation and visualization; modeling and simulation of systems.
Methods and Applications for Modeling and Simulation of Complex Systems: 21st Asia Simulation Conference, AsiaSim 2022, Changsha, China, December 9-11, 2022, Proceedings, Part I (Communications in Computer and Information Science #1712)
by Lin Zhang Xiao Song Wenhui Fan Ni LiThe two-volume set CCIS 1712 and 1713 constitutes the proceedings of the 21st Asian Simulation Conference, AsiaSim 2022, which took place in Changsha, China, in January 2023. Due to the Covid pandemic AsiaSim 2022 has been postponed to January 2023. The 97 papers presented in the proceedings were carefully reviewed and selected from 218 submissions. The contributions were organized in topical sections as follows: Modeling theory and methodology; Continuous system/discrete event system/hybrid system/intelligent system modeling and simulation; Complex systems and open, complex and giant systems modeling and simulation; Integrated natural environment and virtual reality environment modeling and simulation; Networked Modeling and Simulation; Flight simulation, simulator, simulation support environment, simulation standard and simulation system construction; High performance computing, parallel computing, pervasive computing, embedded computing and simulation; CAD/CAE/CAM/CIMS/VP/VM/VR/SBA; Big data challenges and requirements for simulation and knowledge services of big data ecosystem; Artificial intelligence for simulation; Application of modeling/simulation in science/engineering/society/economy /management/energy/transportation/life/biology/medicine etc; Application of modeling/simulation in energy saving/emission reduction, public safety, disaster prevention/mitigation; Modeling/simulation applications in the military field; Modeling/simulation applications in education and training; Modeling/simulation applications in entertainment and sports.
Methods and Applications for Modeling and Simulation of Complex Systems: 21st Asia Simulation Conference, AsiaSim 2022, Changsha, China, December 9-11, 2022, Proceedings, Part II (Communications in Computer and Information Science #1713)
by Lin Zhang Xiao Song Wenhui Fan Ni LiThe two-volume set CCIS 1712 and 1713 constitutes the proceedings of the 21st Asian Simulation Conference, AsiaSim 2022, which took place in Changsha, China, in January 2023. Due to the Covid pandemic AsiaSim 2022 has been postponed to January 2023. The 97 papers presented in the proceedings were carefully reviewed and selected from 218 submissions. The contributions were organized in topical sections as follows: Modeling theory and methodology; Continuous system/discrete event system/hybrid system/intelligent system modeling and simulation; Complex systems and open, complex and giant systems modeling and simulation; Integrated natural environment and virtual reality environment modeling and simulation; Networked Modeling and Simulation; Flight simulation, simulator, simulation support environment, simulation standard and simulation system construction; High performance computing, parallel computing, pervasive computing, embedded computing and simulation; CAD/CAE/CAM/CIMS/VP/VM/VR/SBA; Big data challenges and requirements for simulation and knowledge services of big data ecosystem; Artificial intelligence for simulation; Application of modeling/simulation in science/engineering/society/economy /management/energy/transportation/life/biology/medicine etc; Application of modeling/simulation in energy saving/emission reduction, public safety, disaster prevention/mitigation; Modeling/simulation applications in the military field; Modeling/simulation applications in education and training; Modeling/simulation applications in entertainment and sports.
Methods and Applications for Modeling and Simulation of Complex Systems: 22nd Asia Simulation Conference, AsiaSim 2023, Langkawi, Malaysia, October 25–26, 2023, Proceedings, Part I (Communications in Computer and Information Science #1911)
by Mohamed Sultan Mohamed Ali Fazilah Hassan Noorhazirah Sunar Mohd Ariffanan Mohd Basri Mohd Saiful Azimi Mahmud Mohamad Hafis Izran IshakThis book constitutes the refereed proceedings of the 22nd Asia Simulation Conference on Methods and Applications for Modeling and Simulation of Complex Systems, AsiaSim 2023, held in Langkawi, Malaysia, during October 25–26, 2023.The 77 full papers included in this book were carefully reviewed and selected from 164 submissions. They were organized in topical sections as follows: Modelling and Simulation, Artificial intelligence, Industry 4.0, Digital Twins Modelling, Simulation and Gaming, Simulation for Engineering, Simulation for Sustainable Development, Simulation in Social Sciences.
Methods and Applications for Modeling and Simulation of Complex Systems: 22nd Asia Simulation Conference, AsiaSim 2023, Langkawi, Malaysia, October 25–26, 2023, Proceedings, Part II (Communications in Computer and Information Science #1912)
by Mohamed Sultan Mohamed Ali Fazilah Hassan Noorhazirah Sunar Mohd Ariffanan Mohd Basri Mohd Saiful Azimi Mahmud Mohamad Hafis Izran IshakThis book constitutes the refereed proceedings of the 22nd Asia Simulation Conference on Methods and Applications for Modeling and Simulation of Complex Systems, AsiaSim 2023, held in Langkawi, Malaysia, during October 25–26, 2023.The 77 full papers included in this book were carefully reviewed and selected from 164 submissions. They were organized in topical sections as follows: Modelling and Simulation, Artificial intelligence, Industry 4.0, Digital Twins Modelling, Simulation and Gaming, Simulation for Engineering, Simulation for Sustainable Development, Simulation in Social Sciences.
Methods and Applications for Modeling and Simulation of Complex Systems: 23rd Asia Simulation Conference, AsiaSim 2024, Kobe, Japan, September 17–20, 2024, Proceedings (Communications in Computer and Information Science #2170)
by Liang Li Satoshi Tanaka Seiki Saito Satoshi Takatori Yuichi TamuraThis book constitutes the refereed proceedings of the 23rd Asia Simulation Conference on Methods and Applications for Modeling and Simulation of Complex Systems, AsiaSim 2024, held in Kobe, Japan, during September 17–20, 2024. The 28 full papers presented here, were carefully selected and reviewed from 120 submissions. These papers have been categorized into the following topical sections: Methods for Simulation and Modeling; Simulation for Science, Industry and Society; Interdisciplinary Simulation and Machine Learning; Networks and Complex Systems & Modeling, Simulaiton, and Visualization of Digital Twin.
Methods and Applications of Algorithmic Complexity: Beyond Statistical Lossless Compression (Emergence, Complexity and Computation #44)
by Hector Zenil Fernando Soler Toscano Nicolas GauvritThis book explores a different pragmatic approach to algorithmic complexity rooted or motivated by the theoretical foundations of algorithmic probability and explores the relaxation of necessary and sufficient conditions in the pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance and applicability. Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently coexist for the first time, ranging from the dominant ones based upon popular statistical lossless compression algorithms (such as LZW) to newer approaches that advance, complement, and also pose their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented, and despite their many challenges, some of these methods are better grounded in or motivated by the principles of algorithmic information. The authors propose that the field can make greater contributions to science, causation, scientific discovery, networks, and cognition, to mention a few among many fields, instead of remaining either as a technical curiosity of mathematical interest only or as a statistical tool when collapsed into an application of popular lossless compression algorithms. This book goes, thus, beyond popular statistical lossless compression and introduces a different methodological approach to dealing with algorithmic complexity. For example, graph theory and network science are classic subjects in mathematics widely investigated in the twentieth century, transforming research in many fields of science from economy to medicine. However, it has become increasingly clear that the challenge of analyzing these networks cannot be addressed by tools relying solely on statistical methods. Therefore, model-driven approaches are needed. Recent advances in network science suggest that algorithmic information theory could play an increasingly important role in breaking those limits imposed by traditional statistical analysis (entropy or statistical compression) in modeling evolving complex networks or interacting networks. Further progress on this front calls for new techniques for an improved mechanistic understanding of complex systems, thereby calling out for increased interaction between systems science, network theory, and algorithmic information theory, to which this book contributes.
Methods and Applications of Artificial Intelligence: Dynamic Response, Learning, Random Forest, Linear Regression, Interoperability, Additive Manufacturing and Mechatronics (ISTE Invoiced)
by Abdelkhalak El HamiArtificial Intelligence (AI) is currently one of the most talked-about technologies, both among scientists and in public media. Several factors have contributed to its development in recent years. The first is access to vast quantities of data, such as in the industrial field, the advent of Industry 4.0, which promotes automation and data sharing in several technologies. Another factor is the continuous improvement in computing power thanks to the development of ever more powerful processors and the optimization of algorithms. With these two limitations removed, the focus of most AI developments is on the quality of predictions. The integration of AI into the industrial domain represents an exciting new frontier for innovation. Just as AI has transformed many other sectors, its application to mechanical technologies enables significant improvements in design, manufacturing and quality control processes: from computer-aided design (CAD) to printing parameter optimization, defect detection and real-time monitoring. This type of technology requires computer systems, data with management systems and advanced algorithms which can be used by AIs. In mechanical engineering, AI offers many possibilities in mechanical construction, predictive maintenance, plant monitoring, robotics, additive manufacturing, materials, vibration, etc. Methods and Applications of Artificial Intelligence is dedicated to the methods and applications of AI in mechanical engineering. Each chapter clearly sets out the techniques used and developed and accompanies them with illustrative examples. The book is aimed at students but is also a valuable resource for practicing engineers and research lecturers.
Methods and Applications of Autonomous Experimentation (Chapman & Hall/CRC Computational Science)
by Daniela Ushizima Marcus M. NoackAutonomous Experimentation is poised to revolutionize scientific experiments at advanced experimental facilities. Whereas previously, human experimenters were burdened with the laborious task of overseeing each measurement, recent advances in mathematics, machine learning and algorithms have alleviated this burden by enabling automated and intelligent decision-making, minimizing the need for human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experiences, this book is a practical guide to successful Autonomous Experimentation. Despite the field’s growing potential, there exists numerous myths and misconceptions surrounding Autonomous Experimentation. Combining insights from theorists, machine-learning engineers and applied scientists, this book aims to lay the foundation for future research and widespread adoption within the scientific community. This book is particularly useful for members of the scientific community looking to improve their research methods but also contains additional insights for students and industry professionals interested in the future of the field.
Methods and Experimental Techniques in Computer Engineering
by Francesco Amigoni Viola SchiaffonatiComputing and science reveal a synergic relationship. On the one hand, it is widely evident that computing plays an important role in the scientific endeavor. On the other hand, the role of scientific method in computing is getting increasingly important, especially in providing ways to experimentally evaluate the properties of complex computing systems. This book critically presents these issues from a unitary conceptual and methodological perspective by addressing specific case studies at the intersection between computing and science. The book originates from, and collects the experience of, a course for PhD students in Information Engineering held at the Politecnico di Milano. Following the structure of the course, the book features contributions from some researchers who are working at the intersection between computing and science.
Methods and Models in Mathematical Programming
by F. Hooshmand S. A. MirHassaniThis book focuses on mathematical modeling, describes the process of constructing and evaluating models, discusses the challenges and delicacies of the modeling process, and explicitly outlines the required rules and regulations so that the reader will be able to generalize and reuse concepts in other problems by relying on mathematical logic.Undergraduate and postgraduate students of different academic disciplines would find this book a suitable option preparing them for jobs and research fields requiring modeling techniques. Furthermore, this book can be used as a reference book for experts and practitioners requiring advanced skills of model building in their jobs.
Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions
by Michael Stephan Anand Dubey Avik Santra Souvik Hazra Lorenzo Servadei Thomas StadelmayerMethods and Techniques in Deep Learning Introduces multiple state-of-the-art deep learning architectures for mmWave radar in a variety of advanced applications Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmWave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution. A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrates how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmWave radars. In-depth chapters cover topics including multi-modal deep learning approaches, the elemental blocks required to formulate Bayesian deep learning, how domain adaptation (DA) can be used for improving the performance of machine learning algorithms, and geometric deep learning are used for processing point clouds. In addition, the book: Discusses various advanced applications and how their respective challenges have been addressed using different deep learning architectures and algorithms Describes deep learning in the context of computer vision, natural language processing, sensor processing, and mmWave radar sensors Demonstrates how deep parametric learning reduces the number of trainable parameters and improves the data flow Presents several human-machine interface (HMI) applications such as gesture recognition, human activity classification, human localization and tracking, in-cabin automotive occupancy sensing Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science, and AI.
Methods andAlgorithms in Navigation: Marine Navigation and Safety of Sea Transportation
by Adam Weintrit Tomasz NeumannThe TransNav 2011 Symposium held at the Gdynia Maritime University, Poland in June 2011 has brought together a wide range of participants from all over the world. The program has offered a variety of contributions, allowing to look at many aspects of the navigational safety from various different points of view. Topics presented and discussed at th
Methods for Analyzing Large Neuroimaging Datasets (Neuromethods #218)
by Robert Whelan Hervé LemaîtreThis Open Access volume explores the latest advancements and challenges in standardized methodologies, efficient code management, and scalable data processing of neuroimaging datasets. The chapters in this book are organized in four parts. Part One shows the researcher how to access and download large datasets, and how to compute at scale. Part Two covers best practices for working with large data, including how to build reproducible pipelines and how to use Git. Part Three looks at how to do structural and functional preprocessing data at scale, and Part Four describes various toolboxes for interrogating large neuroimaging datasets, including machine learning and deep learning approaches. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Authoritative and comprehensive, Methods for Analyzing Large Neuroimaging Datasets is a valuable resource that will help researchers obtain the practical knowledge necessary for conducting robust and reproducible analyses of large neuroimaging datasets.
Methods for Appearance-based Loop Closure Detection: Applications To Topological Mapping And Image Mosaicking (Springer Tracts In Advanced Robotics #122)
by Emilio Garcia-Fidalgo Alberto OrtizMapping and localization are two essential tasks in autonomous mobile robotics. Due to the unavoidable noise that sensors present, mapping algorithms usually rely on loop closure detection techniques, which entail the correct identification of previously seen places to reduce the uncertainty of the resulting maps. This book deals with the problem of generating topological maps of the environment using efficient appearance-based loop closure detection techniques. Since the quality of a visual loop closure detection algorithm is related to the image description method and its ability to index previously seen images, several methods for loop closure detection adopting different approaches are developed and assessed. Then, these methods are used in three novel topological mapping algorithms. The results obtained indicate that the solutions proposed attain a better performance than several state-of-the-art approaches. To conclude, given that loop closure detection is also a key component in other research areas, a multi-threaded image mosaicing algorithm is proposed. This approach makes use of one of the loop closure detection techniques previously introduced in order to find overlapping pairs between images and finally obtain seamless mosaics of different environments in a reasonable amount of time.
Methods for Multilevel Analysis and Visualisation of Geographical Networks
by Céline Rozenblat Guy MelanconThis leading-edge study focuses on the latest techniques in analysing and representing the complex, multi-layered data now available to geographers studying urban zones and their populations. The volume tracks the successful results of the SPANGEO Project, which was set up in 2005 to standardize, and share, the syncretic, multinational mapping techniques already developed by geographers and computer scientists. SPANGEO sought new and responsive ways of visualising urban geographical and social data that reflected the fine-grained detail of the inputs. It allowed for visual representation of the large and complex networks and flows which are such an integral feature of the dynamism of urban geography. SPANGEO developed through the 'visual analytics loop' in which geographers collaborated with computer scientists by feeding data into the design of visualisations that in turn spawned the urge to incorporate more varied data into the visualisation. This volume covers all the relevant aspects, from conceptual principles to the tools of network analysis and the actual results flowing from their deployment. Detailed case studies set out in this volume include spatial multi-level analyses of flows in airports and sea ports, as well as the fascinating scientific networks in European cities. The volume shows how the primary concern of geography--the interaction of society with physical space--has been revivified by the complexities of new cartographical and statistical methodologies, which allow for highly detailed mapping and far more powerful computer analysis of spatial relationships.
Methods for Solving Complex Problems in Fluids Engineering
by Can Kang Haixia Liu Yongchao Zhang Ning MaoThis book describes recently developed research methods used to study complex problems in fluid engineering, especially optical flow measurement, flow visualization and numerical methods. It includes a wealth of diagrams and images, and the content is presented in a step-by-step manner from beginning to end, helping readers grasp the central points of the book.The book also presents a number of practical cases, illustrating how the research methods covered can be concretely implemented. Lastly, the book offers a valuable point of departure for pursuing further research.
Methods for Studying Video Games and Religion (Routledge Studies in Religion and Digital Culture)
by Kerstin Radde-Antweiler Xenia Zeiler Vít ŠislerGame studies has been an understudied area within the emerging field of digital media and religion. Video games can reflect, reject, or reconfigure traditionally held religious ideas and often serve as sources for the production of religious practices and ideas. This collection of essays presents a broad range of influential methodological approaches that illuminate how and why video games shape the construction of religious beliefs and practices, and also situates such research within the wider discourse on how digital media intersect with the religious worlds of the 21st century. Each chapter discusses a particular method and its theoretical background, summarizes existing research, and provides a practical case study that demonstrates how the method specifically contributes to the wider study of video games and religion. Featuring contributions from leading and emerging scholars of religion and digital gaming, this book will be an invaluable resource for scholars in the areas of digital culture, new media, religious studies, and game studies across a wide range of disciplines.
Methods for the Analysis of Asymmetric Proximity Data (Behaviormetrics: Quantitative Approaches to Human Behavior #7)
by Donatella Vicari Akinori Okada Giuseppe BoveThis book provides an accessible introduction and practical guidelines to apply asymmetric multidimensional scaling, cluster analysis, and related methods to asymmetric one-mode two-way and three-way asymmetric data. A major objective of this book is to present to applied researchers a set of methods and algorithms for graphical representation and clustering of asymmetric relationships. Data frequently concern measurements of asymmetric relationships between pairs of objects from a given set (e.g., subjects, variables, attributes,…), collected in one or more matrices. Examples abound in many different fields such as psychology, sociology, marketing research, and linguistics and more recently several applications have appeared in technological areas including cybernetics, air traffic control, robotics, and network analysis. The capabilities of the presented algorithms are illustrated by carefully chosen examples and supported by extensive data analyses. A review of the specialized statistical software available for the applications is also provided. This monograph is highly recommended to readers who need a complete and up-to-date reference on methods for asymmetric proximity data analysis.
Methods in Algorithmic Analysis (Chapman & Hall/CRC Computer and Information Science Series)
by Vladimir A. DobrushkinExplores the Impact of the Analysis of Algorithms on Many Areas within and beyond Computer ScienceA flexible, interactive teaching format enhanced by a large selection of examples and exercisesDeveloped from the author’s own graduate-level course, Methods in Algorithmic Analysis presents numerous theories, techniques, and methods used for analyzing algorithms. It exposes students to mathematical techniques and methods that are practical and relevant to theoretical aspects of computer science.After introducing basic mathematical and combinatorial methods, the text focuses on various aspects of probability, including finite sets, random variables, distributions, Bayes’ theorem, and Chebyshev inequality. It explores the role of recurrences in computer science, numerical analysis, engineering, and discrete mathematics applications. The author then describes the powerful tool of generating functions, which is demonstrated in enumeration problems, such as probabilistic algorithms, compositions and partitions of integers, and shuffling. He also discusses the symbolic method, the principle of inclusion and exclusion, and its applications. The book goes on to show how strings can be manipulated and counted, how the finite state machine and Markov chains can help solve probabilistic and combinatorial problems, how to derive asymptotic results, and how convergence and singularities play leading roles in deducing asymptotic information from generating functions. The final chapter presents the definitions and properties of the mathematical infrastructure needed to accommodate generating functions.Accompanied by more than 1,000 examples and exercises, this comprehensive, classroom-tested text develops students’ understanding of the mathematical methodology behind the analysis of algorithms. It emphasizes the important relation between continuous (classical) mathematics and discrete mathematics, which is the basis of computer science.
Methods of Optimization and Systems Analysis for Problems of Transcomputational Complexity
by Ivan V. SergienkoThis work presents lines of investigation and scientific achievements of the Ukrainian school of optimization theory and adjacent disciplines. These include the development of approaches to mathematical theories, methodologies, methods, and application systems for the solution of applied problems in economy, finances, energy saving, agriculture, biology, genetics, environmental protection, hardware and software engineering, information protection, decision making, pattern recognition, self-adapting control of complicated objects, personnel training, etc. The methods developed include sequential analysis of variants, nondifferential optimization, stochastic optimization, discrete optimization, mathematical modeling, econometric modeling, solution of extremum problems on graphs, construction of discrete images and combinatorial recognition, etc. Some of these methods became well known in the world's mathematical community and are now known as classic methods.