Search results for "Distributed Optimization and Learning"
Items we need to order for you
Duality in Vector Optimization
Thecontinuousandincreasinginterestconcerningvectoroptimizationperc- tible in the research community, where contributions dealing with the theory of duality abound lately, constitutes the main motivation that led to writing this book. Decisive was also the research experience of the authors in this ?eld, materialized in a number of works published within the last decade. The need for a book on duality in vector optimization comes from the fact that despite the large amount of papers in journals and proceedings volumes, no book mainly concentrated on this topic was available so far in the scienti?c landscape. There is a considerable presence of books, not all recent releases, on vector optimization in the literature. We mention here the ones due to Chen,HuangandYang(cf. [49]),EhrgottandGandibleux(cf. [65]),Eichfelder (cf. [66]), Goh and Yang (cf. [77]), G.. opfert and Nehse (cf. [80]), G.. opfert, - ahi, Tammer and Z? alinescu (cf. [81]), Jahn (cf. [104]), Kaliszewski (cf. [108]), Luc (cf. [125]), Miettinen (cf. [130]), Mishra, Wang and Lai (cf. [131,132]) and Sawaragi, Nakayama and Tanino (cf. [163]), where vector duality is at most tangentially treated. We hope that from our e?orts will bene?t not only researchers interested in vector optimization, but also graduate and und- graduate students. The framework we consider is taken as general as possible, namely we work in (locally convex) topological vector spaces, going to the usual ?nite - mensional setting when this brings additional insights or relevant connections to the existing literature.
R 4,673.54
Learning and Operating Presto
The Presto community has mushroomed since its origins at Facebook in 2012. But ramping up this distributed SQL query engine can be challenging even for the most experienced engineers. This practical book shows you how to begin Presto operations at your organization to derive insights on datasets wherever they reside. Authors Angelica Lo Duca, Vivek Bharathan, and George Wang explain what Presto is, where it came from, and how it differs from other data warehousing solutions. You'll discover why Facebook, Uber, Twitter, and cloud providers, including AWS, Google Cloud, and Alibaba, use Presto and how you can quickly deploy Presto in production. You'll learn about: Presto security and administration Syntax and connectors Clusters and tuning Troubleshooting: logs, error messages, and more Extending Presto for real-time business insight Extending PrestoDB
R 1,505.00
Handbook of Combinatorial Optimization
Combinatorial (or discrete) optimization is one of the most active fields in the interface of operations research, computer science, and applied math ematics. Combinatorial optimization problems arise in various applications, including communications network design, VLSI design, machine vision, air line crew scheduling, corporate planning, computer-aided design and man ufacturing, database query design, cellular telephone frequency assignment, constraint directed reasoning, and computational biology. Furthermore, combinatorial optimization problems occur in many diverse areas such as linear and integer programming, graph theory, artificial intelligence, and number theory. All these problems, when formulated mathematically as the minimization or maximization of a certain function defined on some domain, have a commonality of discreteness. Historically, combinatorial optimization starts with linear programming. Linear programming has an entire range of important applications including production planning and distribution, personnel assignment, finance, alloca tion of economic resources, circuit simulation, and control systems. Leonid Kantorovich and Tjalling Koopmans received the Nobel Prize (1975) for their work on the optimal allocation of resources. Two important discover ies, the ellipsoid method (1979) and interior point approaches (1984) both provide polynomial time algorithms for linear programming. These algo rithms have had a profound effect in combinatorial optimization. Many polynomial-time solvable combinatorial optimization problems are special cases of linear programming (e.g. matching and maximum flow). In addi tion, linear programming relaxations are often the basis for many approxi mation algorithms for solving NP-hard problems (e.g. dual heuristics).
R 4,711.60
Bio-Inspired Collaborative Intelligent Control and Optimization
This book presents state-of-the-art research advances in the field of biologically inspired cooperative control theories and their applications. It describes various biologically inspired cooperative control and optimization approaches and highlights real-world examples in complex industrial processes. Multidisciplinary in nature and closely integrating theory and practice, the book will be of interest to all university researchers, control engineers and graduate students in intelligent systems and control who wish to learn the core principles, methods, algorithms, and applications.
R 6,228.00
Distributed Computing
This book constitutes the proceedings of the 27th International Symposium on Distributed Computing, DISC 2013, held in Jerusalem, Israel, in October 2013. The 27 full papers presented in this volume were carefully reviewed and selected from 142 submissions; 16 brief announcements are also included. The papers are organized in topical sections named: graph distributed algorithms; topology, leader election, and spanning trees; software transactional memory; shared memory executions; shared memory and storage; gossip and rumor; shared memory tasks and data structures; routing; radio networks and the SINR model; crypto, trust, and influence; and networking.
R 2,223.83
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.
R 3,877.81
Distributed Computer and Communication Networks: Control, Computation, Communications
This book constitutes the refereed post-conference proceedings of the 24th International Conference on Distributed and Computer and Communication Networks, DCCN 2021, held in Moscow, Russia, in September 2021. The 26 revised full papers and 3 revised short papers were carefully reviewed and selected from 151 submissions. The papers cover the following topics: computer and communication networks; analytical modeling of distributed systems; and distributed systems applications.
R 3,332.20
Advances in Bio-inspired Computing for Combinatorial Optimization Problems
"Advances in Bio-inspired Combinatorial Optimization Problems" illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems.Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed.Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive ants; virtual sensitive robots; ant-based techniques for static and dynamic routing problems; stigmergic collaborative agents and learning sensitive agents.This monograph is useful for researchers, students and all people interested in the recent natural computing frameworks. The reader is presumed to have knowledge of combinatorial optimization, graph theory, algorithms and programming. The book should furthermore allow readers to acquire ideas, concepts and models to use and develop new software for solving complex real-life problems.
R 4,679.44
Intelligent Distributed Computing X
This book presents the combined peer-reviewed proceedings of the tenth International Symposium on Intelligent Distributed Computing (IDC’2016), which was held in Paris, France from October 10th to 12th, 2016. The 23 contributions address a range of topics related to theory and application of intelligent distributed computing, including: Intelligent Distributed Agent-Based Systems, Ambient Intelligence and Social Networks, Computational Sustainability, Intelligent Distributed Knowledge Representation and Processing, Smart Networks, Networked Intelligence and Intelligent Distributed Applications, amongst others.
R 6,555.91
Monitoring and Control of Electrical Power Systems using Machine Learning Techniques
Monitoring and Control of Electrical Power Systems using Machine Learning Techniques bridges the gap between advanced machine learning techniques and their application in the control and monitoring of electrical power systems, particularly relevant for heavily distributed energy systems and real-time application. The book reviews key applications of deep learning, spatio-temporal, and advanced signal processing methods for monitoring power quality. This reference introduces guiding principles for the monitoring and control of power quality disturbances arising from integration of power electronic devices and discusses monitoring and control of electrical power systems using benchmark test systems for the creation of bespoke advanced data analytic algorithms.
R 4,855.00
Evolutionary Approach to Machine Learning and Deep Neural Networks
This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
R 3,877.81
Handbook of Moth-Flame Optimization Algorithm
Moth-Flame Optimization algorithm is an emerging meta-heuristic and has been widely used in both science and industry. Solving optimization problem using this algorithm requires addressing a number of challenges, including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters.Handbook of Moth-Flame Optimization Algorithm: Variants, Hybrids, Improvements, and Applications provides an in-depth analysis of this algorithm and the existing methods in the literature to cope with such challenges.Key Features:Reviews the literature of the Moth-Flame Optimization algorithmProvides an in-depth analysis of equations, mathematical models, and mechanisms of the Moth-Flame Optimization algorithmProposes different variants of the Moth-Flame Optimization algorithm to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problemsDemonstrates how to design, develop, and test different hybrids of Moth-Flame Optimization algorithmIntroduces several applications areas of the Moth-Flame Optimization algorithmThis handbook will interest researchers in evolutionary computation and meta-heuristics and those who are interested in applying Moth-Flame Optimization algorithm and swarm intelligence methods overall to different application areas.
R 2,729.76
Multilevel Optimization: Algorithms and Applications
Researchers working with nonlinear programming often claim "the word is non linear" indicating that real applications require nonlinear modeling. The same is true for other areas such as multi-objective programming (there are always several goals in a real application), stochastic programming (all data is uncer tain and therefore stochastic models should be used), and so forth. In this spirit we claim: The word is multilevel. In many decision processes there is a hierarchy of decision makers, and decisions are made at different levels in this hierarchy. One way to handle such hierar chies is to focus on one level and include other levels' behaviors as assumptions. Multilevel programming is the research area that focuses on the whole hierar chy structure. In terms of modeling, the constraint domain associated with a multilevel programming problem is implicitly determined by a series of opti mization problems which must be solved in a predetermined sequence. If only two levels are considered, we have one leader (associated with the upper level) and one follower (associated with the lower level).
R 6,019.21
Adaptive Representations for Reinforcement Learning
This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations. The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class ofoptimization problems. This synthesis is accomplished by customizing evolutionarymethods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators.The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements. This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too.In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methodswith manual representations.
R 3,440.46
Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition
For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach. After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterized by strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets. The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications.
R 4,030.00
Advances in Optimization and Approximation
2. The Algorithm ...59 3. Convergence Analysis ..., ...60 4. Complexity Analysis ...63 5. Conclusions ...67 References ...67 A Simple Proof for a Result of Ollerenshaw on Steiner Trees ...68 Xiufeng Du, Ding-Zhu Du, Biao Gao, and Lixue Qii 1. Introduction ...68 2. In the Euclidean Plane ...69 3. In the Rectilinear Plane ...70 4. Discussion ...-...71 References ...71 Optimization Algorithms for the Satisfiability (SAT) Problem ...72 Jun Gu 1. Introduction ...72 2. A Classification of SAT Algorithms ...7:3 3. Preliminaries ...IV 4. Complete Algorithms and Incomplete Algorithms ...81 5. Optimization: An Iterative Refinement Process ...86 6. Local Search Algorithms for SAT ...89 7. Global Optimization Algorithms for SAT Problem ...106 8. Applications ...137 9. Future Work ...140 10. Conclusions ...141 References ...143 Ergodic Convergence in Proximal Point Algorithms with Bregman Functions ...155 Osman Guier 1. Introduction ...: ...155 2. Convergence for Function Minimization ...158 3. Convergence for Arbitrary Maximal Monotone Operators ...161 References ...163 Adding and Deleting Constraints in the Logarithmic Barrier Method for LP ...166 D. den Hertog, C. Roos, and T. Terlaky 1.Introduction ...16(5 2. The Logarithmic Darrier Method ...lG8 CONTENTS IX 3. The Effects of Shifting, Adding and Deleting Constraints ...171 4. The Build-Up and Down Algorithm ...177 ...5. Complexity Analysis ...180 References ...184 A Projection Method for Solving Infinite Systems of Linear Inequalities ...186 Hui Hu 1. Introduction ...186 2. The Projection Method ...186 3. Convergence Rate ...189 4. Infinite Systems of Convex Inequalities ...191 5. Application ...193 References ...
R 4,671.04
Aggregation in Large-Scale Optimization
When analyzing systems with a large number of parameters, the dimen sion of the original system may present insurmountable difficulties for the analysis. It may then be convenient to reformulate the original system in terms of substantially fewer aggregated variables, or macrovariables. In other words, an original system with an n-dimensional vector of states is reformulated as a system with a vector of dimension much less than n. The aggregated variables are either readily defined and processed, or the aggregated system may be considered as an approximate model for the orig inal system. In the latter case, the operation of the original system can be exhaustively analyzed within the framework of the aggregated model, and one faces the problems of defining the rules for introducing macrovariables, specifying loss of information and accuracy, recovering original variables from aggregates, etc. We consider also in detail the so-called iterative aggregation approach. It constructs an iterative process, at· every step of which a macroproblem is solved that is simpler than the original problem because of its lower dimension. Aggregation weights are then updated, and the procedure passes to the next step. Macrovariables are commonly used in coordinating problems of hierarchical optimization.
R 3,038.63
Continuous and Distributed Systems II
As in the previous volume on the topic, the authors close the gap between abstract mathematical approaches, such as applied methods of modern algebra and analysis, fundamental and computational mechanics, nonautonomous and stochastic dynamical systems, on the one hand and practical applications in nonlinear mechanics, optimization, decision making theory and control theory on the other.Readers will also benefit from the presentation of modern mathematical modeling methods for the numerical solution of complicated engineering problems in biochemistry, geophysics, biology and climatology. This compilation will be of interest to mathematicians and engineers working at the interface of these fields. It presents selected works of the joint seminar series of Lomonosov Moscow State University and the Institute for Applied System Analysis at National Technical University of Ukraine “Kyiv Polytechnic Institute”. The authors come from Brazil, Germany, France, Mexico, Spain, Poland, Russia, Ukraine and the USA.
R 4,397.17
Distributed Computer and Communication Networks: Control, Computation, Communications
This book constitutes the refereed post-conference proceedings of the 25th International Conference on Distributed and Computer and Communication Networks, DCCN 2022, held in Moscow, Russia, in September 26–29, 2022. The 31 revised full papers and 2 revised short papers were carefully reviewed and selected from 130 submissions. The papers cover the following topics: computer and communication networks; analytical modeling of distributed systems; and distributed systems applications.
R 3,533.75
Optimization Techniques and Applications with Examples
A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniques in optimization that encompass the broadness and diversity of the methods (traditional and new) and algorithms. The author—a noted expert in the field—covers a wide range of topics including mathematical foundations, optimization formulation, optimality conditions, algorithmic complexity, linear programming, convex optimization, and integer programming. In addition, the book discusses artificial neural network, clustering and classifications, constraint-handling, queueing theory, support vector machine and multi-objective optimization, evolutionary computation, nature-inspired algorithms and many other topics. Designed as a practical resource, all topics are explained in detail with step-by-step examples to show how each method works. The book’s exercises test the acquired knowledge that can be potentially applied to real problem solving. By taking an informal approach to the subject, the author helps readers to rapidly acquire the basic knowledge in optimization, operational research, and applied data mining. This important resource: Offers an accessible and state-of-the-art introduction to the main optimization techniquesContains both traditional optimization techniques and the most current algorithms and swarm intelligence-based techniquesPresents a balance of theory, algorithms, and implementationIncludes more than 100 worked examples with step-by-step explanations Written for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, Optimization Techniques and Applications with Examples is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization.
R 4,492.00
Principles of Concurrent and Distributed Programming
The latest edition of a classic text on concurrency and distributed programming – from a winner of the ACM/SIGCSE Award for Outstanding Contribution to Computer Science Education.
R 2,188.00
Computational Optimization
Computational Optimization: A Tribute to Olvi Mangasarian serves as an excellent reference, providing insight into some of the most challenging research issues in the field. This collection of papers covers a wide spectrum of computational optimization topics, representing a blend of familiar nonlinear programming topics and such novel paradigms as semidefinite programming and complementarity-constrained nonlinear programs. Many new results are presented in these papers which are bound to inspire further research and generate new avenues for applications. An informal categorization of the papers includes: Algorithmic advances for special classes of constrained optimization problems Analysis of linear and nonlinear programs Algorithmic advances B- stationary points of mathematical programs with equilibrium constraints Applications of optimization Some mathematical topics Systems of nonlinear equations.
R 4,650.32
Learning-by-doing und endogenes Wachstum
Lerneffekte haben im mikrookonomischen Bereich eine wichtige Bedeutung sowohl fur individuelle Lernentscheidungen als auch fur strategische Unternehmensentscheidungen. Dieses Buch widmet sich der theoretischen Analyse der makrookonomischen Wachstumseffekte der Erfahrungsakkumulation. Basierend auf dem Konzept der Erfahrungskurve wird das Learning-by-doing als Kuppelprodukt der Produktionsaktivitat in Modelle endogenen Wachstums implementiert. Die Trennung des Lernens von der sonst ublichen Bindung an die Sachkapitalakkumulation fuhrt zur unabhangigen Kontrolle der Erfahrungsakkumulation uber die Arbeitszeit. Die schulische Bildung von Humankapital erfolgt in einem zweiten Sektor. Die dynamisch optimale Wahl der Freizeit, der Arbeitszeit und der Aufteilung des Humankapitals zwischen schulischer Ausbildung und Produktion wird hergeleitet.
R 1,948.86
R 1,069.00
Optimization in Engineering Sciences
The purpose of this book is to present the main metaheuristics and approximate and stochastic methods for optimization of complex systems in Engineering Sciences. It has been written within the framework of the European Union project ERRIC (Empowering Romanian Research on Intelligent Information Technologies), which is funded by the EU’s FP7 Research Potential program and has been developed in co-operation between French and Romanian teaching researchers. Through the principles of various proposed algorithms (with additional references) this book allows the reader to explore various methods of implementation such as metaheuristics, local search and populationbased methods. It examines multi-objective and stochastic optimization, as well as methods and tools for computer-aided decision-making and simulation for decision-making.
R 6,772.00
Reinforcement Learning Algorithms: Analysis and Applications
This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications. The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universität Darmstadt. The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.
R 5,074.45
Machine Learning and Principles and Practice of Knowledge Discovery in Databases
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online. The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops:Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021)Workshop on Parallel, Distributed and Federated Learning (PDFL 2021)Workshop on Graph Embedding and Mining (GEM 2021)Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021)Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021)Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021)Workshop on Bias and Fairness in AI (BIAS 2021)Workshop on Workshop on Active Inference (IWAI 2021)Workshop on Machine Learning for Cybersecurity (MLCS 2021)Workshop on Machine Learning in Software Engineering (MLiSE 2021)Workshop on MIning Data for financial applications (MIDAS 2021)Sixth Workshop on Data Science for Social Good (SoGood 2021)Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021)Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020)Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021)
R 3,941.25
Classical and Recent Aspects of Power System Optimization
Classical and Recent Aspects of Power System Optimization presents conventional and meta-heuristic optimization methods and algorithms for power system studies. The classic aspects of optimization in power systems, such as optimal power flow, economic dispatch, unit commitment and power quality optimization are covered, as are issues relating to distributed generation sizing, allocation problems, scheduling of renewable resources, energy storage, power reserve based problems, efficient use of smart grid capabilities, and protection studies in modern power systems. The book brings together innovative research outcomes, programs, algorithms and approaches that consolidate the present state and future challenges for power.
R 4,414.00
Modelling Distributed Systems
This textbook guides students through algebraic specification and verification of distributed systems, and some of the most prominent formal verification techniques. The author employs µCRL as the vehicle, a language developed to combine process algebra and abstract data types. The book evolved from introductory courses on protocol verification taught to undergraduate and graduate students of computer science, and the text is supported throughout with examples and exercises. Full solutions are provided in an appendix, while exercise sheets, lab exercises, example specifications and lecturer slides are available on the author's website.
R 2,151.69
Combinatorial Optimization
This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Symposium on Combinatorial Optimization, ISCO 2018, held in Marrakesh, Marocco, in April 2018. The 35 revised full papers presented in this book were carefully reviewed and selected from 75 submissions. The symposium aims to bring together researchers from all the communities related to combinatorial optimization, including algorithms and complexity, mathematical programming and operations research.
R 3,152.68
Nonlinear Combinatorial Optimization
Graduate students and researchers in applied mathematics, optimization, engineering, computer science, and management science will find this book a useful reference which provides an introduction to applications and fundamental theories in nonlinear combinatorial optimization. Nonlinear combinatorial optimization is a new research area within combinatorial optimization and includes numerous applications to technological developments, such as wireless communication, cloud computing, data science, and social networks. Theoretical developments including discrete Newton methods, primal-dual methods with convex relaxation, submodular optimization, discrete DC program, along with several applications are discussed and explored in this book through articles by leading experts.
R 3,827.09
Distributed, Ambient and Pervasive Interactions
This book constitutes the refereed proceedings of the 11th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2023, held as part of the 25th International Conference on Human-Computer Interaction, HCII 2023, which took place as an hybrid event in Copenhagen, Denmark, in July 2023.A total of 1578 papers and 396 posters have been accepted for publication in the HCII 2023 proceedings from a total of 7472 submissions. The 60 papers included in the DAPI 2023 proceedings were organized in topical sections as follows:Part I: Designing and evaluating intelligent environments; user experience in intelligent environments; pervasive data;Part II: Smart cities and environment preservation; media, art and culture in intelligent environments; supporting health, learning, work and everyday life.
R 3,544.19
Optimization, Control, and Applications of Stochastic Systems
This volume provides a general overview of discrete- and continuous-time Markov control processes and stochastic games, along with a look at the range of applications of stochastic control and some of its recent theoretical developments. These topics include various aspects of dynamic programming, approximation algorithms, and infinite-dimensional linear programming. In all, the work comprises 18 carefully selected papers written by experts in their respective fields. Optimization, Control, and Applications of Stochastic Systems will be a valuable resource for all practitioners, researchers, and professionals in applied mathematics and operations research who work in the areas of stochastic control, mathematical finance, queueing theory, and inventory systems. It may also serve as a supplemental text for graduate courses in optimal control and dynamic games.
R 2,282.34
Lectures on Convex Optimization
This book provides a comprehensive, modern introduction to convex optimization, a field that is becoming increasingly important in applied mathematics, economics and finance, engineering, and computer science, notably in data science and machine learning.Written by a leading expert in the field, this book includes recent advances in the algorithmic theory of convex optimization, naturally complementing the existing literature. It contains a unified and rigorous presentation of the acceleration techniques for minimization schemes of first- and second-order. It provides readers with a full treatment of the smoothing technique, which has tremendously extended the abilities of gradient-type methods. Several powerful approaches in structural optimization, including optimization in relative scale and polynomial-time interior-point methods, are also discussed in detail.Researchers in theoretical optimization as well as professionals working on optimization problems will find this book very useful. It presents many successful examples of how to develop very fast specialized minimization algorithms. Based on the author’s lectures, it can naturally serve as the basis for introductory and advanced courses in convex optimization for students in engineering, economics, computer science and mathematics.
R 1,875.71
Intelligent Decision Systems in Large-Scale Distributed Environments
One of the most challenging issues for the intelligent decision systems is to effectively manage the large-scale complex distributed environments such as computational clouds, grids, ad hoc and P2P networks, under the different types of users, their relations, and real-world uncertainties. In this context the IT resources and services usually belong to different owners (institutions, enterprises, or individuals) and are managed by different administrators. These administrators conform to different sets of rules and configuration directives, and can impose different usage policies on the system users. Additionally, uncertainties are presented in various types of information that are incomplete, imprecise, fragmentary or overloading, which hinders the full and precise determination of the evaluation criteria, their subsequent and selection, the assignment scores, and eventually the final integrated decision result.This book presents new ideas, analysis, implementations and evaluation of the next generation intelligent techniques for solving complex decision problems in large-scale distributed systems. In 15 chapters several important formulations of the decision problems in heterogeneous environments are identified and a review of the recent approaches, from game theoretical models and computational intelligent techniques, such as genetic, memetic and evolutionary algorithms, to intelligent multi-agent systems and networking are presented. We believe that this volume will serve as a reference for the students, researchers and industry practitioners working in or are interested in joining interdisciplinary works in the areas of intelligent decision systems using emergent distributed computing paradigms. It will also allow newcomers to grasp key concerns and potential solutions on the selected topics.
R 6,820.06
Distributed Constraint Satisfaction
Distributed Constraint Satisfaction gives an overview of Constraint Satisfaction Problems (CSPs), adapts related search algorithms and consistency algorithms for applications to multi-agent systems, and consolidates recent research devoted to cooperation in such systems. The techniques introduced are applied to various problems in multi-agent systems. Among the new approaches is a hybrid-type algorithm for weak-commitment search combining backtracking and iterative improvement. Also, an extension of the basic CSP formalization called "Partial CSP" is introduced in order to handle over-constrained CSPs.
R 1,534.32
Advances in Optimization and Applications
This book constitutes the refereed proceedings of the 11th International Conference on Optimization and Applications, OPTIMA 2020, held in September – October 2020. Due to the COVID-19 pandemic the conference was held online. The 18 revised full papers presented were carefully reviewed and selected from 60 submissions. The papers are organized in topical sections on global optimization; combinatorial and discrete optimization; optimal control; optimization in economy, finance and social sciences; applications.
R 2,169.62
Distributed Computer and Communication Networks
This book constitutes the refereed proceedings of the 25th International Conference on Distributed Computer and Communication Networks, DCCN 2022, held in Moscow, Russia, in September 2022.The 27 full papers and 2 short papers included in this book were carefully reviewed and selected from 130 submissions. They were organized in topical sections as follows: Distributed Systems Applications, Computer and Communication Networks, Analytical Modeling of Distributed Systems.
R 3,525.52
Control Flow and Data Flow: Concepts of Distributed Programming
In a time of multiprocessor machines, message switching networks and process control programming tasks, the foundations of programming distributed systems are among the central challenges for computing sci enti sts. The foundati ons of di stributed programming compri se all the fasci nating questions of computing science: the development of adequate com putational , conceptual and semantic model s for distributed systems, specification methods, verification techniques, transformation rules, the development of suitable representations by programming languages, evaluation and execution of programs describing distributed systems. Being the 7th in a series of ASI Summer Schools at Marktoberdorf, these lectures concentrated on distributed systems. Already during the previous Summer School s at Marktoberdorf aspects of di stributed systems were important periodical topics. The rising interest in distributed systems, their design and implementation led to a considerable amount of research in this area. This is impressively demonstrated by the broad spectrum of the topics of the papers in this vol ume, although they are far from being comprehensive for the work done in the area of distributed systems. Distributed systems are extraordinarily complex and allow many distinct viewpoints. Therefore the literature on distributed systems sometimes may look rather confusing to people not working in the field. Nevertheless there is no reason for resignation: the Summer School was able to show considerable convergence in ideas, approaches and concepts for distributed systems.
R 3,260.06
Stabilization, Safety, and Security of Distributed Systems
This book constitutes the refereed proceedings of the 18th International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2016, held in Lyon, France, in November 2016.This year the Program Committee was organized into three groups reflecting the major trends related to self-* systems: (a) Self-* and Autonomic Computing, (b)Foundations, and (c) Networks, Multi-Agent Systems, and Mobility.
R 2,197.70
Intelligent Distributed Computing, Systems and Applications
This book represents the peer-reviewed proceedings of the Second International Symposium on Intelligent Distributed Computing – IDC 2008 held in Catania, Italy during September 18-19, 2008. The 35 contributions in this book address many topics related to intelligent and distributed computing, systems and applications, including: adaptivity and learning; agents and multi-agent systems; argumentation; auctions; case-based reasoning; collaborative systems; data structures; distributed algorithms; formal modeling and verification; genetic and immune algorithms; grid computing; information extraction, annotation and integration; network and security protocols; mobile and ubiquitous computing; ontologies and metadata; P2P computing; planning; recommender systems; rules; semantic Web; services and processes; trust and social computing; virtual organizations; wireless networks; XML technologies.
R 4,658.40
Distributed Algorithms
This book constitutes the proceedings of the 9th International Workshop on Distributed Algorithms, WDAG '95, held in Le Mont-Saint-Michel, France in September 1995.Besides four invited contributions, 18 full revised research papers are presented, selected from a total of 48 submissions during a careful refereeing process. The papers document the progress achieved in the area since the predecessor workshop (LNCS 857); they are organized in sections on asynchronous systems, networks, shared memory, Byzantine failures, self-stabilization, and detection of properties.
R 1,565.19
Local Search in Combinatorial Optimization
In the past three decades, local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization that is attracting ever-increasing attention. Local search is still the method of choice for NP-hard problems as it provides a robust approach for obtaining high-quality solutions to problems of a realistic size in reasonable time. Local Search in Combinatorial Optimization covers local search and its variants from both a theoretical and practical point of view, each topic discussed by a leading authority. This book is an important reference and invaluable source of inspiration for students and researchers in discrete mathematics, computer science, operations research, industrial engineering, and management science. In addition to the editors, the contributors are Mihalis Yannakakis, Craig A. Tovey, Jan H. M. Korst, Peter J. M. van Laarhoven, Alain Hertz, Eric Taillard, Dominique de Werra, Heinz Muhlenbein, Carsten Peterson, Bo Soderberg, David S. Johnson, Lyle A. McGeoch, Michel Gendreau, Gilbert Laporte, Jean-Yves Potvin, Gerard A. P. Kindervater, Martin W. P. Savelsbergh, Edward J. Anderson, Celia A. Glass, Chris N. Potts, C. L.Liu, Peichen Pan, Iiro Honkala, and Patric R. J. Ostergard.
R 3,847.07
Distributed and Parallel Systems
Distributed and Parallel Systems: From Cluster to Grid Computing, is an edited volume based on DAPSYS 2006, the 6th Austrian-Hungarian Workshop on Distributed and Parallel Systems, which is dedicated to all aspects of distributed and parallel computing. The workshop was held in conjunction with the 2nd Austrian Grid Symposium in Innsbruck, Austria in September 2006. This book is designed for a professional audience composed of practitioners and researchers in industry. It is also suitable for advanced-level students in computer science.
R 4,642.97
