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Applications of Optimization and Machine Learning in Image Processing and IoT
This book presents state-of-the-art of optimization algorithms followed by internet of things (IoT) fundamentals. The applications of machine learning and IoT are explored, with topics such as optimization, algorithms, machine learning in image processing and IoT.Applications of Optimization and Machine Learning in Image Processing and IoT is a complete reference source, providing the latest research findings and solutions for optimization and machine learning algorithms. The chapters examine and discuss machine learning, and IoT and image processing field.Key Features:· Includes fundamental concepts towards advanced applications in machine learning and IoT.· Discusses potential and challenges of machine learning for IoT and optimization· Reviews recent advancements in diverse researches on computer vision, networking and optimization field.· Presents latest technologies like machine learning in image processing and IoTThis book has been written considering readers in academia, engineering, IT specialists, researchers, industrial professionals, and students, and is a great reference for those just starting out in the field as well as those at an advanced level.
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Distributed Computing
This book constitutes the refereed proceedings of the 20th International Symposium on Distributed Computing, DISC 2006. The book presents 35 revised full papers together with 1 invited paper and 13 announcements of ongoing works, all carefully selected for inclusion in the book. The entire scope of current issues in distributed computing is addressed, ranging from foundational and theoretical topics to algorithms and systems issues and to applications in various fields.
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Optimal Load Balancing in Distributed Computer Systems
An important consideration in improving the performance of a distributed computer system is the balancing of the load between the host computers. Load balancing may be either static or dynamic; static balancing strategies are generally based on information about the system's average behavior rather than its actual current state, while dynamic strategies react to the current state when making transfer decisions. Although it is often conjectured that dynamic load balancing outperforms static, careful investigation shows that this view is not always valid. Recent research on the problem of optimal static load balancing is clearly and intuitively presented, with coverage of distributed computer system models, problem formulation in load balancing, and effective algorithms for implementing optimization. Providing a thorough understanding of both static and dynamic strategies, this book will be of interest to all researchers and practitioners working to optimize performance in distributed computer systems.
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Duality in Optimization and Variational Inequalities
This comprehensive volume covers a wide range of duality topics ranging from simple ideas in network flows to complex issues in non-convex optimization and multicriteria problems. In addition, it examines duality in the context of variational inequalities and vector variational inequalities, as generalizations to optimization. Duality in Optimization and Variational Inequalities is intended for researchers and practitioners of optimization with the aim of enhancing their understanding of duality. It provides a wider appreciation of optimality conditions in various scenarios and under different assumptions. It will enable the reader to use duality to devise more effective computational methods, and to aid more meaningful interpretation of optimization and variational inequality problems.
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Stochastic Modeling and Optimization
This books covers the broad range of research in stochastic models and optimization. Applications presented include networks, financial engineering, production planning, and supply chain management. Each contribution is aimed at graduate students working in operations research, probability, and statistics.
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Agent-Based Optimization
This volume presents a collection of original research works by leading specialists focusing on novel and promising approaches in which the multi-agent system paradigm is used to support, enhance or replace traditional approaches to solving difficult optimization problems. The editors have invited several well-known specialists to present their solutions, tools, and models falling under the common denominator of the agent-based optimization. The book consists of eight chapters covering examples of application of the multi-agent paradigm and respective customized tools to solve difficult optimization problems arising in different areas such as machine learning, scheduling, transportation and, more generally, distributed and cooperative problem solving.
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Reactive Search and Intelligent Optimization
Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics.Individual chapters cover reacting on the neighborhood; reacting on the annealing schedule; reactive prohibitions; model-based search; reacting on the objective function; relationships between reactive search and reinforcement learning; and much more. Each chapter is structured to show basic issues and algorithms; the parameters critical for the success of the different methods discussed; and opportunities for the automated tuning of these parameters.
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Optimization and Multiobjective Control of Time-Discrete Systems
Richard Bellmann developed a theory of dynamic programming which is for many reasons still in the center of great interest. The authors present a new approach in the ?eld of the optimization and multi-objective control of time-discrete systems which is closely related to the work of Richard Bellmann. They develop their own concept and their extension to the optimization and multi-objective control of time-discrete systems as well as to dynamic networks and multilayered structures are very stimulating for further research. Di?erent perspectives of discrete control and optimal dynamic ?ow problems on networks are treated and characterized. Together with the algorithmic solutions a framework of multi-objective control problems is - rived. The conclusion with a real world example underlines the necessity and - portance of their theoretic framework. As they come back to the classical Bellmann concept of dynamic programming they stress and honor his basic concept without debase their own work. Multilayereddecisionprocessesaspartofthedesignandanalysisofcomplexsystems and networks will be essential in many ways and ?elds in the future.
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Linear and Integer Optimization
Presenting a strong and clear relationship between theory and practice, Linear and Integer Optimization: Theory and Practice is divided into two main parts. The first covers the theory of linear and integer optimization, including both basic and advanced topics. Dantzig’s simplex algorithm, duality, sensitivity analysis, integer optimization models, and network models are introduced. More advanced topics also are presented including interior point algorithms, the branch-and-bound algorithm, cutting planes, complexity, standard combinatorial optimization models, the assignment problem, minimum cost flow, and the maximum flow/minimum cut theorem.The second part applies theory through real-world case studies. The authors discuss advanced techniques such as column generation, multiobjective optimization, dynamic optimization, machine learning (support vector machines), combinatorial optimization, approximation algorithms, and game theory.Besides the fresh new layout and completely redesigned figures, this new edition incorporates modern examples and applications of linear optimization. The book now includes computer code in the form of models in the GNU Mathematical Programming Language (GMPL). The models and corresponding data files are available for download and can be readily solved using the provided online solver.This new edition also contains appendices covering mathematical proofs, linear algebra, graph theory, convexity, and nonlinear optimization. All chapters contain extensive examples and exercises. This textbook is ideal for courses for advanced undergraduate and graduate students in various fields including mathematics, computer science, industrial engineering, operations research, and management science.
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Mathematical Optimization Theory and Operations Research: Recent Trends
This book constitutes refereed proceedings of the 22nd International Conference on Mathematical Optimization Theory and Operations Research: Recent Trends, MOTOR 2023, held in Ekaterinburg, Russia, during July 2–8, 2023. The 28 full papers and one invited paper presented in this volume were carefully reviewed and selected from a total of 61 submissions. The papers in the volume are organized according to the following topical headings: mathematical programming; stochastic optimization; discrete and combinatorial optimization; operations research; optimal control and mathematical economics; and optimization in machine learning.
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Distributed Algorithms
This book constitutes the refereed proceedings of the 10th International Workshop on Distributed Algorithms, WDAG '96, held in Bologna, Italy, in October 1996.The volume presents 21 revised full papers selected from 75 submissions together with invited contributions by Butler Lampson and Domenico Ferrari. The volume reflects the transition of the area from formerly being the domain of a small research community to becoming the dominant computing paradigm and an industrial technology. Among the topics covered are all current aspects of distributed algorithms ranging from theory to applications such as distributed intelligent agents.
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Coordination of Distributed Energy Resources in Microgrids
Coordination of Distributed Energy Resources in Microgrids: Optimisation, control, and hardware-in-the-loop validation provides a structured overview of research into techniques for managing microgrids with distributed energy resources (DERs). The DERs including distributed generators, energy storage systems, and flexible loads are posing both challenges and opportunities to microgrids' security, planning, operation, and control. Advanced operation and control techniques are needed to coordinate these components in the microgrids and maintain power quality, as well as keeping the system economically feasible. This book is for researchers and students in the area of smart grids, power engineering, and control engineering, as well as for advanced students, transmission network and grid operators. It focuses on cutting-edge techniques for secure, economic, and robust operation and control of microgrids. Effective coordination of DERs on both temporal and spatial scales are introduced in detail. Topics covered include comprehensive mathematical models of DERs and microgrids, sizing and siting of DERs under uncertainties, stochastic and robust optimisation for active and reactive power dispatch of DERs in microgrids, distributed coordinated control, and hardware-in-the-loop tests for validation of control algorithms.
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Advances in Stochastic and Deterministic Global Optimization
Current research results in stochastic and deterministic global optimization including single and multiple objectives are explored and presented in this book by leading specialists from various fields. Contributions include applications to multidimensional data visualization, regression, survey calibration, inventory management, timetabling, chemical engineering, energy systems, and competitive facility location. Graduate students, researchers, and scientists in computer science, numerical analysis, optimization, and applied mathematics will be fascinated by the theoretical, computational, and application-oriented aspects of stochastic and deterministic global optimization explored in this book.This volume is dedicated to the 70th birthday of Antanas Žilinskas who is a leading world expert in global optimization. Professor Žilinskas's research has concentrated on studying models for the objective function, the development and implementation of efficient algorithms for global optimization with single and multiple objectives, and application of algorithms for solving real-world practical problems.
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Networked and Distributed Predictive Control
Networked and Distributed Predictive Control presents rigorous, yet practical, methods for the design of networked and distributed predictive control systems – the first book to do so. The design of model predictive control systems using Lyapunov-based techniques accounting for the influence of asynchronous and delayed measurements is followed by a treatment of networked control architecture development. This shows how networked control can augment dedicated control systems in a natural way and takes advantage of additional, potentially asynchronous and delayed measurements to maintain closed loop stability and significantly to improve closed-loop performance. The text then shifts focus to the design of distributed predictive control systems that cooperate efficiently in computing optimal manipulated input trajectories that achieve desired stability, performance and robustness specifications but spend a fraction of the time required by centralized control systems. Key features of this book include: • new techniques for networked and distributed control system design; • insight into issues associated with networked and distributed predictive control and their solution; • detailed appraisal of industrial relevance using computer simulation of nonlinear chemical process networks and wind- and solar-energy-generation systems; and • integrated exposition of novel research topics and rich resource of references to significant recent work. A full understanding of Networked and Distributed Predictive Control requires a basic knowledge of differential equations, linear and nonlinear control theory and optimization methods and the book is intended for academic researchers and graduate students studying control and for process control engineers. The constant attention to practical matters associated with implementation of the theory discussed will help each of these groups understand the application of the book’s methods in greater depth.
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Deep Learning Techniques and Optimization Strategies in Big Data Analytics
Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
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Evolutionary Computation for Modeling and Optimization
Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets.Lots of applications and test problems, including a biotechnology chapter.
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Cooperative Control and Optimization
A cooperative system is defined to be multiple dynamic entities that share information or tasks to accomplish a common, though perhaps not singular, objective. Examples of cooperative control systems might include: robots operating within a manufacturing cell, unmanned aircraft in search and rescue operations or military surveillance and attack missions, arrays of micro satellites that form a distributed large aperture radar, employees operating within an organization, and software agents. The term entity is most often associated with vehicles capable of physical motion such as robots, automobiles, ships, and aircraft, but the definition extends to any entity concept that exhibits a time dependent behavior. Critical to cooperation is communication, which may be accomplished through active message passing or by passive observation. It is assumed that cooperation is being used to accomplish some common purpose that is greater than the purpose of each individual, but we recognize that the individual may have other objectives as well, perhaps due to being a member of other caucuses. This implies that cooperation may assume hierarchical forms as well. The decision-making processes (control) are typically thought to be distributed or decentralized to some degree. For if not, a cooperative system could always be modeled as a single entity. The level of cooperation may be indicated by the amount of information exchanged between entities. Cooperative systems may involve task sharing and can consist of heterogeneous entities. Mixed initiative systems are particularly interesting heterogeneous systems since they are composed of humans and machines. Finally, one is often interested in how cooperative systems perform under noisy or adversary conditions. In December 2000, the Air Force Research Laboratory and the University of Florida successfully hosted the first Workshop on Cooperative Control and Optimization in Gainesville, Florida. This book contains selected refereed papers summarizing the participants' research in control and optimization of cooperative systems. Audience: Faculty, graduate students, and researchers in optimization and control, computer sciences and engineering.
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Control of Distributed Parameter and Stochastic Systems
In the mathematical treatment of many problems which arise in physics, economics, engineering, management, etc., the researcher frequently faces two major difficulties: infinite dimensionality and randomness of the evolution process. Infinite dimensionality occurs when the evolution in time of a process is accompanied by a space-like dependence; for example, spatial distribution of the temperature for a heat-conductor, spatial dependence of the time-varying displacement of a membrane subject to external forces, etc. Randomness is intrinsic to the mathematical formulation of many phenomena, such as fluctuation in the stock market, or noise in communication networks. Control theory of distributed parameter systems and stochastic systems focuses on physical phenomena which are governed by partial differential equations, delay-differential equations, integral differential equations, etc., and stochastic differential equations of various types. This has been a fertile field of research with over 40 years of history, which continues to be very active under the thrust of new emerging applications. Among the subjects covered are: Control of distributed parameter systems; Stochastic control; Applications in finance/insurance/manufacturing; Adapted control; Numerical approximation . It is essential reading for applied mathematicians, control theorists, economic/financial analysts and engineers.
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Integrated Approach to Modeling and Optimization in Engineering and Science
An Integrated Approach to Modeling and Optimization in Engineering and Science examines the effects of experimental design, mathematical modeling, and optimization processes for solving many different problems. The Experimental Design Method, Central Composite, Full Factorial, Taguchi, Box-Behnken, and D-Optimal methods are used, and the effects of the datasets obtained by these methods on mathematical modeling are investigated.This book will help graduates and senior undergraduates in courses on experimental design, modeling, optimization, and interdisciplinary engineering studies. It will also be of interest to research and development engineers and professionals working in scientific institutions based on design, modeling, and optimization.
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Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization
This groundbreaking book extends traditional approaches of risk measurement and portfolio optimization by combining distributional models with risk or performance measures into one framework. Throughout these pages, the expert authors explain the fundamentals of probability metrics, outline new approaches to portfolio optimization, and discuss a variety of essential risk measures. Using numerous examples, they illustrate a range of applications to optimal portfolio choice and risk theory, as well as applications to the area of computational finance that may be useful to financial engineers.
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Optimization and Applications
This book constitutes the refereed proceedings of the 9th International Conference on Optimization and Applications, OPTIMA 2018, held in Petrovac, Montenegro, in October 2018.The 35 revised full papers and the one short paper presented were carefully reviewed and selected from 103 submissions. The papers are organized in topical sections on mathematical programming; combinatorial and discrete optimization; optimal control; optimization in economy, finance and social sciences; applications.
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Swarm Stability and Optimization
Swarming species such as flocks of birds or schools of fish exhibitfascinating collective behaviors during migration and predatoravoidance. Similarly, engineered multi-agent dynamic systems such as groups of autonomous ground, underwater, or air vehicles (“vehicle swarms”) exhibit sophisticated collective behaviors while maneuvering.In this book we show how to model and control a wide range of suchmulti-agent dynamic systems and analyze their collective behaviorusing both stability theoretic and simulation-based approaches. Inparticular, we investigate problems such as group aggregation, socialforaging, formation control, swarm tracking, distributed agreement,and engineering optimization inspired by swarm behavior.
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Distributed Multiple Description Coding
This book examines distributed video coding (DVC) and multiple description coding (MDC), two novel techniques designed to address the problems of conventional image and video compression coding. Covering all fundamental concepts and core technologies, the chapters can also be read as independent and self-sufficient, describing each methodology in sufficient detail to enable readers to repeat the corresponding experiments easily. Topics and features: provides a broad overview of DVC and MDC, from the basic principles to the latest research; covers sub-sampling based MDC, quantization based MDC, transform based MDC, and FEC based MDC; discusses Sleplian-Wolf coding based on Turbo and LDPC respectively, and comparing relative performance; includes original algorithms of MDC and DVC; presents the basic frameworks and experimental results, to help readers improve the efficiency of MDC and DVC; introduces the classical DVC system for mobile communications, providing the developmental environment in detail.
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Advances in Intelligent and Distributed Computing
This book presents the proceedings of the 1st International Symposium on Intelligent and Distributed Computing, IDC 2007, held in Craiova, Romania, October 2007. Coverage includes: autonomous and adaptive computing; data mining and knowledge discovery; distributed problem solving and decision making; e-business, e-health and e-learning; genetic algorithms; image processing; information retrieval; intelligence in mobile and ubiquitous computing.
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Deep Learning Generalization
This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies. It delves deeply into how machine learning models, particularly deep neural networks, achieve robust performance on unseen data. Key topics include balancing model complexity, addressing overfitting and underfitting, and understanding modern phenomena such as the double descent curve and implicit regularization.The book offers a holistic perspective by addressing the four critical components of model training: data, model architecture, objective functions, and optimization processes. It combines mathematical rigor with hands-on guidance, introducing practical implementation techniques using PyTorch to bridge the gap between theory and real-world applications. For instance, the book highlights how regularized deep learning models not only achieve better predictive performance but also assume a more compact and efficient parameter space. Structured to accommodate a progressive learning curve, the content spans foundational concepts like statistical learning theory to advanced topics like Neural Tangent Kernels and overparameterization paradoxes.By synthesizing classical and modern views of generalization, the book equips readers to develop a nuanced understanding of key concepts while mastering practical applications.For academics, the book serves as a definitive resource to solidify theoretical knowledge and explore cutting-edge research directions. For industry professionals, it provides actionable insights to enhance model performance systematically. Whether you're a beginner seeking foundational understanding or a practitioner exploring advanced methodologies, this book offers an indispensable guide to achieving robust generalization in deep learning.
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Distributed Computing
DISC, the International Symposium on DIStributed Computing, is an annual forum for research presentations on all facets of distributed computing. DISC 2001 was held on Oct 3-5, 2001, in Lisbon, Portugal. This volume includes 23 contributed papers. It is expected that these papers will be submitted in more polished form to fully refereed scienti?c journals. The extended abstracts of this year's invited lectures, by Gerard LeLann and David Peleg, will appear in next year's proceedings. We received 70 regular submissions. These submissions were read and eval- ted by the program committee, with the help of external reviewers when needed. Overall, the quality of the submissions was excellent, and we were unable to - cept many deserving papers. This year's Best Student Paper award goes to Yong-Jik Kim for the paper "A Time Complexity Bound for Adaptive Mutual Exclusion" by Yong-Jik Kim and James H. Anderson. October 2001 Jennifer Welch Organizing Committee Chair: Luis Rodrigues (University of Lisbon) Publicity: Paulo Ver'?ssimo (University of Lisbon) Treasurer: Filipe Araujo ' (University of Lisbon) Web: Alexandre Pinto (University of Lisbon) Registration: Hugo Miranda (University of Lisbon) Steering Committee Faith Fich (U.of Toronto) Michel Raynal (vice-chair) (IRISA) Maurice Herlihy (Brown U. ) Andr'e Schiper (chair) (EPF Lausanne) Prasad Jayanti (Dartmouth) Jennifer Welch (Texas A&M U. ) Shay Kutten (Technion) Program Committee Marcos K. Aguilera (Compaq SRC) Mark Moir (Sun Microsystems Laboratories) Lorenzo Alvisi (U. Texas, Austin) Stephane Perennes (CNRS U.
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Distributed Algorithms
This book constitutes the refereed proceedings of the 11th International Workshop on Distributed Algorithms, WDAG '97, held in Saarbrücken, Germany, in September 1997.The volume presents 20 revised full papers selected from 59 submissions. Also included are three invited papers by leading researchers. The papers address a variety of current issues in the area of distributed algorithms and, more generally, distributed systems such as various particular algorithms, randomized computing, routing, networking, load balancing, scheduling, message-passing, shared-memory systems, communication, graph algorithms, etc.
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Practical Methods of Optimization
Fully describes optimization methods that are currently most valuable in solving real-life problems. Since optimization has applications in almost every branch of science and technology, the text emphasizes their practical aspects in conjunction with the heuristics useful in making them perform more reliably and efficiently. To this end, it presents comparative numerical studies to give readers a feel for possibile applications and to illustrate the problems in assessing evidence. Also provides theoretical background which provides insights into how methods are derived. This edition offers revised coverage of basic theory and standard techniques, with updated discussions of line search methods, Newton and quasi-Newton methods, and conjugate direction methods, as well as a comprehensive treatment of restricted step or trust region methods not commonly found in the literature. Also includes recent developments in hybrid methods for nonlinear least squares; an extended discussion of linear programming, with new methods for stable updating of LU factors; and a completely new section on network programming. Chapters include computer subroutines, worked examples, and study questions.
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Distributed Systems and Applications of Information Filtering and Retrieval
This volume focuses on new challenges in distributed Information Filtering and Retrieval. It collects invited chapters and extended research contributions from the special session on Information Filtering and Retrieval: Novel Distributed Systems and Applications (DART) of the 4th International Conference on Knowledge Discovery and Information Retrieval (KDIR 2012), held in Barcelona, Spain, on 4-7 October 2012.The main focus of DART was to discuss and compare suitable novel solutions based on intelligent techniques and applied to real-world applications. The chapters of this book present a comprehensive review of related works and state of the art. Authors, both practitioners and researchers, shared their results in several topics such as "Multi-Agent Systems", "Natural Language Processing", "Automatic Advertisement", "Customer Interaction Analytics", "Opinion Mining".Contributions have been careful reviewed by experts in the area, who also gave useful suggestions to improve the quality of the volume.
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Modeling and Optimization: Theory and Applications
This book features a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in B ethlehem, Pennsylvania, USA between August 16-18, 2017. The conference brought together a diverse group of researchers and practitioners working on both theoretical and practical aspects of continuous and discrete optimization. Topics covered include algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and address the application of deterministic andstochastic optimization techniques in energy, finance, logistics, analytics, health, and other important fields. The selected contributions in this book illustrate the broad diversity of ideas discussed at the meeting.
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Parallel Computing and Mathematical Optimization
This special volume contains the Proceedings of a Workshop on "Parallel Algorithms and Transputers for Optimization" which was held at the University of Siegen, on November 9, 1990. The purpose of the Workshop was to bring together those doing research on 2.lgorithms for parallel and distributed optimization and those representatives from industry and business who have an increasing demand for computing power and who may be the potential users of nonsequential approaches. In contrast to many other conferences, especially North-American, on parallel processing and supercomputers the main focus of the contributions and discussion was "problem oriented". This view reflects the following philosophy: How can the existing computing infrastructure (PC's, workstations, local area networks) of an institution or a company be used for parallel and/or distributed problem solution in optimization. This volume of the LECfURE NOTES ON ECONOMICS AND MA THEMA TICAL SYSTEMS contains most of the papers presented at the workshop, plus some additional invited papers covering other important topics related to this workshop. The papers appear here grouped according to four general areas. (1) Solution of optimization problems using massive parallel systems (data parallelism). The authors of these papers are: Lootsma; Gehne. (II) Solution of optimization problems using coarse-grained parallel approaches on multiprocessor systems (control parallelism). The authors of these papers are: Bierwirth, Mattfeld, and Stoppler; Schwartz; Boden, Gehne, and Grauer; and Taudes and Netousek.
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Metaheuristics for Bi-level Optimization
This book provides a complete background on metaheuristics to solve complex bi-level optimization problems (continuous/discrete, mono-objective/multi-objective) in a diverse range of application domains. Readers learn to solve large scale bi-level optimization problems by efficiently combining metaheuristics with complementary metaheuristics and mathematical programming approaches. Numerous real-world examples of problems demonstrate how metaheuristics are applied in such fields as networks, logistics and transportation, engineering design, finance and security.
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Distributed Artificial Intelligence
This book constitutes the refereed proceedings of the Second International Conference on Distributed Artificial Intelligence, DAI 2020, held in Nanjing, China, in October 2020.The 9 full papers presented in this book were carefully reviewed and selected from 22 submissions. DAI aims at bringing together international researchers and practitioners in related areas including general AI, multiagent systems, distributed learning, computational game theory, etc., to provide a single, high-profile, internationally renowned forum for research in the theory and practice of distributed AI. Due to the Corona pandemic this event was held virtually.
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Elements of Optimization
This book attempts to present the concepts which underlie the various optimization procedures which are commonly used. It is written primarily for those scientists such as economists, operations researchers, and en gineers whose main tools of analysis involve optimization techniques and who possess a (not very sharp) knowledge of one or one-and-a-half year's calculus through partial differentiation and Taylor's theorem and some acquaintance with elementary vector and matrix terminology. Such a scientist is frequently confronted with expressions such as Lagrange multi pliers, first-and second-order conditions, linear programming and activity analysis, duality, the Kuhn-Tucker conditions, and, more recently, dy namic programming and optimal control. He or she uses or needs to use these optimization techniques, and would like to feel more comfortable with them through better understanding of their underlying mathematical concepts, but has no immediate use for a formal theorem-proof treatment which quickly abstracts to a general case of n variables and uses a style and terminology that are discouraging to people who are not mathematics majors. The emphasis of this book is on clarity and plausibility. Through examples which are worked out step by step in detail, I hope to illustrate some tools which will be useful to scientists when they apply optimization techniques to their problems. Most of the chapters may be read independently of each other-with the exception of Chapter 6, which depends on Chapter 5. For instance, the reader will find little or no difficulty in reading Chapter 8 without having read the previous chapters.
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Parallel and Distributed Computational Intelligence
Francisco Fern' andez de Vega and Erick Cantu- ' Paz The growing success of biologically inspired algorithms in solving large and complex problems has spawned many interesting areas of research. Over the years, one of the mainstays in bio-inspired research has been the exploi- tion of parallel and distributed environments to speedup computations and to enrich the algorithms. From the early days of research on bio-inspired algorithms, their inherently parallel nature was recognized and di?erent p- allelization approaches have been explored. Parallel algorithms promise - ductions in execution time and open the door to solve increasingly larger problems. But parallel platforms also inspire new bio-inspired parallel al- rithms that, while similar to their sequential counterparts, explore search spaces di?erently and o?er improvements in solution quality. Our objective in editing this book was to assemble a sample of the best work in parallel and distributed biologically inspired algorithms. We invited researchers in di?erent domains to submit their work. We aimed to include diverse topics to appeal to a wide audience.Some of the chapters sum- rize work that has been ongoing for several years, while others describe more recent exploratory work. Collectively, these works o?er a global snapshot of the most recent e?orts of bioinspired algorithms' researchers aiming at pr- iting from parallel and distributed computer architectures-including GPUs, Clusters, Grids, volunteer computing and p2p networks as well as multi-core processors.
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Optimization and Applications
This book constitutes the refereed proceedings of the 13th International Conference on Optimization and Applications, OPTIMA 2022, held in Petrovac, Montenegro, during September 26–30, 2022.The 17 full papers and presented were carefully reviewed and selected from 43 submissions. They were organized into the following as follows: mathematical programming; global optimization; discrete and combinatorial optimization; optimal control; optimization and data analysis; and game theory and mathematical economics.
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System Modelling and Optimization
Proceedings volume contains carefully selected papers presented during the 17th IFIP Conference on System Modelling and Optimization. Optimization theory and practice, optimal control, system modelling, stochastic optimization, and technical and non-technical applications of the existing theory are among areas mostly addressed in the included papers. Main directions are treated in addition to several survey papers based on invited presentations of leading specialists in the respective fields. Publication provides state-of-the-art in the area of system theory and optimization and points out several new areas (e.g fuzzy set, neural nets), where classical optimization topics intersects with computer science methodology.
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Nonlinear Optimization and Related Topics
This volume contains the edited texts of the lectures presented at the Workshop on Nonlinear Optimization held in Erice, Sicily, at the "G. Stampacchia" School of Mathematics of the "E. Majorana" Centre for Scientific Culture, June 23 -July 2, 1998. In the tradition of these meetings, the main purpose was to review and discuss recent advances and promising research trends concerning theory, algorithms and innovative applications in the field of Nonlinear Optimization, and of related topics such as Convex Optimization, Nonsmooth Optimization, Variational Inequalities and Complementarity Problems. The meeting was attended by 83 people from 21 countries. Besides the lectures, several formal and informal discussions took place. The result was a wide and deep knowledge of the present research tendencies in the field. We wish to express our appreciation for the active contribution of all the par ticipants in the meeting. Our gratitude is due to the Ettore Majorana Centre in Erice, which offered its facilities and rewarding environment: its staff was certainly instrumental for the success of the meeting. Our gratitude is also due to Francisco Facchinei and Massimo Roma for the effort and time devoted as members of the Organising Committee. We are indebted to the Italian National Research Council, and in particular to the Group on Functional Analysis and its Applications and to the Committees on Engineering Sciences and on Information Sciences and Technolo gies for their financial support. Finally, we address our thanks to Kluwer Academic Publishers for having offered to publish this volume.
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Handbook on Modelling for Discrete Optimization
This book aims to demonstrate and detail the pervasive nature of Discrete Optimization. The handbook couples the difficult, critical-thinking aspects of mathematical modeling with the hot area of discrete optimization. It is done with an academic treatment outlining the state-of-the-art for researchers across the domains of the Computer Science, Math Programming, Applied Mathematics, Engineering, and Operations Research. The book utilizes the tools of mathematical modeling, optimization, and integer programming to solve a broad range of modern problems.
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Distributed Computer and Communication Networks
This book constitutes the refereed proceedings of the 19th International Conference on Distributed and Computer and Communication Networks, DCCN 2016, held in Moscow, Russia, in November 2016. The 50 revised full papers and the 6 revised short papers presented were carefully reviewed and selected from 141 submissions. The papers cover the following topics: computer and communication networks architecture optimization; control in computer and communication networks; performance and QoS/QoE evaluation in wireless networks; analytical modeling and simulation of next-generation communications systems; queuing theory and reliability theory applications in computer networks; wireless 4G/5G networks, cm- and mm-wave radio technologies; RFID technology and its application in intellectual transportation networks; internet of things, wearables, and applications of distributed information systems; probabilistic and statistical models in information systems; mathematical modeling of high-tech systems; mathematical modeling and control problems; distributed and cloud computing systems, big data analytics.
R 2,236.49
Advances in Optimization and Numerical Analysis
In January 1992, the Sixth Workshop on Optimization and Numerical Analysis was held in the heart of the Mixteco-Zapoteca region, in the city of Oaxaca, Mexico, a beautiful and culturally rich site in ancient, colonial and modern Mexican civiliza tion. The Workshop was organized by the Numerical Analysis Department at the Institute of Research in Applied Mathematics of the National University of Mexico in collaboration with the Mathematical Sciences Department at Rice University, as were the previous ones in 1978, 1979, 1981, 1984 and 1989. As were the third, fourth, and fifth workshops, this one was supported by a grant from the Mexican National Council for Science and Technology, and the US National Science Foundation, as part of the joint Scientific and Technical Cooperation Program existing between these two countries. The participation of many of the leading figures in the field resulted in a good representation of the state of the art in Continuous Optimization, and in an over view of several topics including Numerical Methods for Diffusion-Advection PDE problems as well as some Numerical Linear Algebraic Methods to solve related pro blems. This book collects some of the papers given at this Workshop.
R 3,037.18
Spatially Explicit Hyperparameter Optimization for Neural Networks
Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is written for researchers of the GIScience field as well as social science subjects.
R 6,576.58
Numerical and Evolutionary Optimization – NEO 2017
This book features 15 chapters based on the Numerical and Evolutionary Optimization (NEO 2017) workshop, held from September 27 to 29 in the city of Tijuana, Mexico. The event gathered researchers from two complimentary fields to discuss the theory, development and application of state-of-the-art techniques to address search and optimization problems. The lively event included 7 invited talks and 64 regular talks covering a wide range of topics, from evolutionary computer vision and machine learning with evolutionary computation, to set oriented numeric and steepest descent techniques. Including research submitted by the NEO community, the book provides informative and stimulating material for future research in the field.
R 9,543.01
Advances in Optimization and Numerical Analysis
In January 1992, the Sixth Workshop on Optimization and Numerical Analysis was held in the heart of the Mixteco-Zapoteca region, in the city of Oaxaca, Mexico, a beautiful and culturally rich site in ancient, colonial and modern Mexican civiliza tion. The Workshop was organized by the Numerical Analysis Department at the Institute of Research in Applied Mathematics of the National University of Mexico in collaboration with the Mathematical Sciences Department at Rice University, as were the previous ones in 1978, 1979, 1981, 1984 and 1989. As were the third, fourth, and fifth workshops, this one was supported by a grant from the Mexican National Council for Science and Technology, and the US National Science Foundation, as part of the joint Scientific and Technical Cooperation Program existing between these two countries. The participation of many of the leading figures in the field resulted in a good representation of the state of the art in Continuous Optimization, and in an over view of several topics including Numerical Methods for Diffusion-Advection PDE problems as well as some Numerical Linear Algebraic Methods to solve related pro blems. This book collects some of the papers given at this Workshop.
R 3,037.01
R 1,665.00
Optimal Measurement Methods for Distributed Parameter System Identification
For dynamic distributed systems modeled by partial differential equations, existing methods of sensor location in parameter estimation experiments are either limited to one-dimensional spatial domains or require large investments in software systems. With the expense of scanning and moving sensors, optimal placement presents a critical problem.Optimal Measurement Methods for Distributed Parameter System Identification discusses the characteristic features of the sensor placement problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, culminating in the most comprehensive and timely treatment of the issue available. By presenting a step-by-step guide to theoretical aspects and to practical design methods, this book provides a sound understanding of sensor location techniques.Both researchers and practitioners will find the case studies, the proposed algorithms, and the numerical examples to be invaluable. This text also offers results that translate easily to MATLAB and to Maple. Assuming only a basic familiarity with partial differential equations, vector spaces, and probability and statistics, and avoiding too many technicalities, this is a superb resource for researchers and practitioners in the fields of applied mathematics, electrical, civil, geotechnical, mechanical, chemical, and environmental engineering.
R 4,001.63
