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Algorithms - ESA '94
This volume presents the proceedings of the Second Annual European Symposium on Algorithms (ESA '94), held near Utrecht, The Netherlands in September 1994. ESA is the premier European conference on algorithms.The 42 papers presented were selected from a total of 159 submissions and reflect many of the current directions in algorithm research. The volume is organized into chapters on automatic graph drawing and rendering, spanners and Steiner trees, efficient data structures and complexity analysis, graph algorithms, distributed algorithms, computational geometry and its applications, operations research and combinatorial optimization, parallel computation and interconnection networks, complexity theory, and text processing
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Algorithms - ESA'99
The 7th Annual European Symposium on Algorithms (ESA ’99) is held in Prague, Czech Republic, July 16-18, 1999. This continued the tradition of the meetings which were held in – 1993 Bad Honnef (Germany) – 1994 Utrecht (Netherlands) – 1995 Corfu (Greece) – 1996 Barcelona (Spain) – 1997 Graz (Austria) – 1998 Venice (Italy) (The proceedingsof previousESA meetings were publishedas Springer LNCS v- umes 726, 855, 979, 1136, 1284, 1461.) In the short time of its history ESA (like its sister meeting SODA) has become a popular and respected meeting. The call for papers stated that the “Symposium covers research in the use, design, and analysis of ef?cient algorithms and data structures as it is carried out in c- puter science, discrete applied mathematics and mathematical programming. Papers are solicited describing original results in all areas of algorithmic research, including but not limited to: Approximation Algorithms; Combinatorial Optimization; Compu- tional Biology; Computational Geometry; Databases and Information Retrieval; Graph and Network Algorithms; Machine Learning; Number Theory and Computer Algebra; On-line Algorithms; Pattern Matching and Data Compression; Symbolic Computation.
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Basic Data Structures and Program Statements
This book covers C-Programming focussing on its practical side. Volume 1 deals mainly with basic data structures, algorithms and program statements. An extensive use of figures and examples help to give a clear description of concepts help the reader to gain a systematic understanding of the language.
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Applied Evolutionary Algorithms in Java
This book is intended for students, researchers, and professionals interested in evolutionary algorithms at graduate and postgraduate level. No mathematics beyond basic algebra and Cartesian graphs methods is required, as the aim is to encourage applying the JAVA toolkit to develop an appreciation of the power of these techniques.
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WALCOM: Algorithms and Computation
This book constitutes the refereed proceedings of the Second International Workshop on Algorithms and Computation, WALCOM 2008, held in Dhaka, Bangladesh, in February 2008.The 19 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 57 submissions. The papers feature original research in the areas of algorithms and data structures, combinatorial algorithms, graph drawings and graph algorithms, parallel and distributed algorithms, string algorithms, computational geometry, graphs in bioinformatics and computational biology. The papers are organized in topical sections on bioinformatics algorithms, computational geometry and graph drawing, graph algorithms, and algorithm engineering.
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VLSI Architecture for Concurrent Data Structures
Concurrent data structures simplify the development of concurrent programs by encapsulating commonly used mechanisms for synchronization and commu nication into data structures. This thesis develops a notation for describing concurrent data structures, presents examples of concurrent data structures, and describes an architecture to support concurrent data structures. Concurrent Smalltalk (CST), a derivative of Smalltalk-80 with extensions for concurrency, is developed to describe concurrent data structures. CST allows the programmer to specify objects that are distributed over the nodes of a concurrent computer. These distributed objects have many constituent objects and thus can process many messages simultaneously. They are the foundation upon which concurrent data structures are built. The balanced cube is a concurrent data structure for ordered sets. The set is distributed by a balanced recursive partition that maps to the subcubes of a binary 7lrcube using a Gray code. A search algorithm, VW search, based on the distance properties of the Gray code, searches a balanced cube in O(log N) time. Because it does not have the root bottleneck that limits all tree-based data structures to 0(1) concurrency, the balanced cube achieves 0C.:N) con currency. Considering graphs as concurrent data structures, graph algorithms are pre sented for the shortest path problem, the max-flow problem, and graph parti tioning. These algorithms introduce new synchronization techniques to achieve better performance than existing algorithms.
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Problems and Exercises in Discrete Mathematics
Many years of practical experience in teaching discrete mathematics form the basis of this text book. Part I contains problems on such topics as Boolean algebra, k-valued logics, graphs and networks, elements of coding theory, automata theory, algorithms theory, combinatorics, Boolean minimization and logical design. The exercises are preceded by ample theoretical background material. For further study the reader is referred to the extensive bibliography. Part II follows the same structure as Part I, and gives helpful hints and solutions. Audience:This book will be of great value to undergraduate students of discrete mathematics, whereas the more difficult exercises, which comprise about one-third of the material, will also appeal to postgraduates and researchers.
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Programming Algorithms in Lisp
Master algorithms programming using Lisp, including the most important data structures and algorithms. This book also covers the essential tools that help in the development of algorithmic code to give you all you need to enhance your code.Programming Algorithms in Lisp shows real-world engineering considerations and constraints that influence the programs that use these algorithms. It includes practical use cases of the applications of the algorithms to a variety of real-world problems. What You Will LearnProgram algorithms using the Lisp programming languageWork with data structures, arrays, key-values, hash-tables, trees, graphs, and moreUse dynamic programming Program using stringsWork with approximations and compression Who This Book Is For Intermediate Lisp programmers wanting to do algorithms programming. A very experienced non-Lisp programmer may be able to benefit from this book as well.
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Applied Algorithms
This book constitutes the refereed proceedings of the Third International Conference on Applied Algorithms, ICAA 2026, held in Kolkata, India, during January 7–9, 2026.The 33 full papers presented in this book were carefully reviewed and selected from 92 submissions. They focus on the design, analysis, implementation, and experimental evaluation of efficient algorithms and data structures aimed at solving practical, real-world problems.
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Applied Algorithms
This book LNCS constitutes the refereed proceedings of the Second International Conference on Applied Algorithms, ICAA 2025, held in Kolkata, India, during January 8–10, 2025.The 26 full papers and 4 short papers are carefully reviewed and selected from 93 submissions. This conference sought original contributions related to the design, analysis, implementation, and experimental evaluation of efficient algorithms and data structures for problems.
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Algorithms
This text explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal. Features include: The use of boxes to strengthen the narrative: pieces that provide historical context, descriptions of how the algorithms are used in practice, and excursions for the mathematically sophisticated. Carefully chosen advanced topics that can be skipped in a standard one-semester course, but can be covered in an advanced algorithms course or in a more leisurely two-semester sequence. An accessible treatment of linear programming introduces students to one of the greatest achievements in algorithms. An optional chapter on the quantum algorithm for factoring provides a unique peephole into this exciting topic. "Algorithms" is an outstanding undergraduate text, equally informed by the historical roots and contemporary applications of its subject.
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Algorithms and Programming
This text is structured in a problem-solution format that requires the student to think through the programming process. New to the second edition are additional chapters on suffix trees, games and strategies, and Huffman coding as well as an Appendix illustrating the ease of conversion from Pascal to C.
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Algorithms and Complexity
This book constitutes the refereed proceedings of the Third Italian Conference on Algorithms and Complexity, CIAC'97, held in Rome, Italy in March 1997.The 25 revised full papers included in the volume were carefully selected from a total of 74 submissions; also included is an invited paper and an invited abstract. All in all, the papers present an interesting snapshot of current research activities and recent results in theory and applications of sequential, distributed, and parallel algorithms, data structures, and computational complexity.
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Data Structures in Depth Using C++
Understand and implement data structures and bridge the gap between theory and application. This book covers a wide range of data structures, from basic arrays and linked lists to advanced trees and graphs, providing readers with in-depth insights into their implementation and optimization in C++.You’ll explore crucial topics to optimize performance and enhance their careers in software development. In today's environment of growing complexity and problem scale, a profound grasp of C++ data structures, including efficient data handling and storage, is more relevant than ever. This book introduces fundamental principles of data structures and design, progressing to essential concepts for high-performance application.Finally, you’ll explore the application of data structures in real-world scenarios, including case studies and use in machine learning and big data. This practical, step-by-step approach, featuring numerous code examples, performance analysis and best practices, is written with a wide range of C++ programmers in mind. So, if you’re looking to solve complex data structure problems using C++, this book is your complete guide.What You Will LearnWrite robust and efficient C++ code.Apply data structures in real-world scenarios.Transition from basic to advanced data structuresUnderstand best practices and performance analysis.Design a flexible and efficient data structure library.Who This Book is For Software developers and engineers seeking to deepen their knowledge of data structures and enhanced coding efficiency, and ideal for those with a foundational understanding of C++ syntax. Secondary audiences include entry-level programmers seeking deeper dive into data structures, enhancing their skills, and preparing them for more advanced programming tasks. Finally, computer science students or programmers aiming to transition to C++ may find value in this book.
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Combinatorial Algorithms
This book constitutes the thoroughly referred post-workshop proceedings of the 23rd International Workshop on Combinatorial Algorithms, IWOCA 2012, held in Krishnankoil, Tamil Nadu, India, in July 2012.The 32 revised full papers presented were carefully reviewed and selected from a total of 88 submissions. The papers are organized in topical sections in algorithms and data Structures, applications (including Bioinformatics, Networking, etc.), combinatorics of words and strings, combinatorial optimization, combinatorial enumeration, decompositions and combinatorial designs, complexity theory (structural and computational), computational biology and graph theory and combinatorics submissions.
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Purely Functional Data Structures
Most books on data structures assume an imperative language like C or C++. However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme. This book describes data structures from the point of view of functional languages, with examples, and presents design techniques so that programmers can develop their own functional data structures. It includes both classical data structures, such as red-black trees and binomial queues, and a host of new data structures developed exclusively for functional languages. All source code is given in Standard ML and Haskell, and most of the programs can easily be adapted to other functional languages. This handy reference for professional programmers working with functional languages can also be used as a tutorial or for self-study.
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Graph Algorithms
Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they’re used for building dynamic network models or forecasting real-world behavior. Mark Needham and Amy Hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. You’ll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics. Learn how graph analytics reveal more predictive elements in today’s data Understand how popular graph algorithms work and how they’re applied Use sample code and tips from more than 20 graph algorithm examples Learn which algorithms to use for different types of questions Explore examples with working code and sample datasets for Spark and Neo4j Create an ML workflow for link prediction by combining Neo4j and Spark
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Algorithms and Programming
This text is structured in a problem-solution format that requires the student to think through the programming process. New to the second edition are additional chapters on suffix trees, games and strategies, and Huffman coding as well as an Appendix illustrating the ease of conversion from Pascal to C.
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Algorithms for Data Science
This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
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Design and Analysis of Algorithms
These are my lecture notes from CS681: Design and Analysis of Algo rithms, a one-semester graduate course I taught at Cornell for three consec utive fall semesters from '88 to '90. The course serves a dual purpose: to cover core material in algorithms for graduate students in computer science preparing for their PhD qualifying exams, and to introduce theory students to some advanced topics in the design and analysis of algorithms. The material is thus a mixture of core and advanced topics. At first I meant these notes to supplement and not supplant a textbook, but over the three years they gradually took on a life of their own. In addition to the notes, I depended heavily on the texts • A. V. Aho, J. E. Hopcroft, and J. D. Ullman, The Design and Analysis of Computer Algorithms. Addison-Wesley, 1975. • M. R. Garey and D. S. Johnson, Computers and Intractibility: A Guide to the Theory of NP-Completeness. w. H. Freeman, 1979. • R. E. Tarjan, Data Structures and Network Algorithms. SIAM Regional Conference Series in Applied Mathematics 44, 1983. and still recommend them as excellent references.
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Combinatorial Algorithms
This book constitutes the thoroughly refereedpost-workshop proceedings for the 26 International Workshop on combinatorialAlgorithms, IWOCA 2015, held in Verona, Italy, in October 2015.The 29 revised full papers presented were carefullyreviewed and selected from a total of 90 submissions. The topics of the papersinclude algorithms and data structures (including sequential, parallel,distributed, approximation, probabilistic, randomised, and on-line algorithms),algorithms on strings and graphs; applications (bioinformatics, music analysis,networking, and others); combinatorics on words; combinatorial enumeration;combinatorial optimization; complexity theory; computational biology;compression and information retrieval; cryptography and information security;decompositions and combinatorial designs; discrete and computational geometry;graph drawing and labeling; graph theory.
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Algorithms and Computation
This book constitutes the refereed proceedings of the 23rd International Symposium on Algorithms and Computation, ISAAC 2012, held in Taipei, Taiwan, in December 2012. The 68 revised full papers presented together with three invited talks were carefully reviewed and selected from 174 submissions for inclusion in the book. This volume contains topics such as graph algorithms; online and streaming algorithms; combinatorial optimization; computational complexity; computational geometry; string algorithms; approximation algorithms; graph drawing; data structures; randomized algorithms; and algorithmic game theory.
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Algorithms
This is the proceedings of the SIGAL International Symposium on Algorithms held at CSK Information Education Center, Tokyo, Japan, August 16-18, 1990. SIGAL (Special Interest Group on Algorithms) was organized within the Information Processing Society of Japan in 1988 to encourage research in the field of discrete algorithms, and held 6-8 research meetings each year. This symposium is the first international symposium organized by SIGAL. In response to the call for papers, 88 papers were submitted from around the world. The program committee selected 34 for presentation at the symposium. The symposium also included 5 invited lectures and 10 invited presentations. The subjects of the papers range widely in the field of discrete algorithms in theoretical computer science. Keywords for these subjects are: computational geometry, graph algorithms, complexity theory, parallel algorithms, distributed computing, and computational algebra.
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JavaTech, an Introduction to Scientific and Technical Computing with Java
JavaTech is a practical introduction to the Java programming language with an emphasis on the features that benefit technical computing. After presenting the basics of object-oriented programming in Java, it examines introductory topics such as graphical interfaces and thread processes. It goes on to review network programming and develops Web client-server examples for tasks such as monitoring remote devices. The focus then shifts to distributed computing with RMI. Finally, it examines how Java programs can access the local platform and interact with hardware. Topics include combining native code with Java, communication via serial lines, and programming embedded processors. An extensive web site supports the book with additional instructional materials. JavaTech demonstrates the ease with which Java can be used to create powerful network applications and distributed computing applications. It will be used as a textbook for programming courses, and by researchers who need to learn Java for a particular task.
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Mathematics of Discrete Structures for Computer Science
Mathematics plays a key role in computer science, some researchers would consider computers as nothing but the physical embodiment of mathematical systems. And whether you are designing a digital circuit, a computer program or a new programming language, you need mathematics to be able to reason about the design -- its correctness, robustness and dependability. This book covers the foundational mathematics necessary for courses in computer science.The common approach to presenting mathematical concepts and operators is to define them in terms of properties they satisfy, and then based on these definitions develop ways of computing the result of applying the operators and prove them correct. This book is mainly written for computer science students, so here the author takes a different approach: he starts by defining ways of calculating the results of applying the operators and then proves that they satisfy various properties. After justifying his underlying approach the author offers detailed chapters covering propositional logic, predicate calculus, sets, relations, discrete structures, structured types, numbers, and reasoning about programs.The book contains chapter and section summaries, detailed proofs and many end-of-section exercises -- key to the learning process. The book is suitable for undergraduate and graduate students, and although the treatment focuses on areas with frequent applications in computer science, the book is also suitable for students of mathematics and engineering.
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50 Algorithms Every Programmer Should Know
Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaulPurchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesFamiliarize yourself with advanced deep learning architecturesExplore newer topics, such as handling hidden bias in data and algorithm explainabilityGet to grips with different programming algorithms and choose the right data structures for their optimal implementationBook DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works.You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them.Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use.You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT.Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnDesign algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisDelve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examplesCreate a recommendation engine that suggests relevant movies to subscribersGrasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMsWho this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful.Python programming experience is a must, knowledge of data science will be helpful but not n
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Algorithms and Computation
This volume gives the proceedings of ISAAC '92, the ThirdInternational Symposium on Algorithms and Computation, heldin Nagoya, Japan, December 1992.The first symposium was held in Tokyo in 1990, as the firstinternational symposium organized by SIGAL (Special InterestGroupon Algorithms in the Information Processing Society ofJapan) to serve as anannual international forum in Asia forresearchers in the area of algorithms. The second symposiumwas held in Taipei, Taiwan in 1991, where it was decidedthat computation would be included in the main scope of thesymposium and that ISAAC would be its name.ISAAC '92 focuses on topics in design and analysis ofalgorithms, computational complexity, and theory ofcomputation, including algorithms and data structures,parallel/distributed computing, automata and formallanguages, probabilistic/approximation algorithms,computability and complexity, term rewriting systems, andcomputational geometry. The volume contains the acceptedcontributed papers and the invited papers.
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Introduction To The Analysis Of Algorithms, An (3rd Edition)
A successor to the first and second editions, this updated and revised book is a leading companion guide for students and engineers alike, specifically software engineers who design algorithms. While succinct, this edition is mathematically rigorous, covering the foundations for both computer scientists and mathematicians with interest in the algorithmic foundations of Computer Science.Besides expositions on traditional algorithms such as Greedy, Dynamic Programming and Divide & Conquer, the book explores two classes of algorithms that are often overlooked in introductory textbooks: Randomised and Online algorithms — with emphasis placed on the algorithm itself. The book also covers algorithms in Linear Algebra, and the foundations of Computation.The coverage of Randomized and Online algorithms is timely: the former have become ubiquitous due to the emergence of cryptography, while the latter are essential in numerous fields as diverse as operating systems and stock market predictions.While being relatively short to ensure the essentiality of content, a strong focus has been placed on self-containment, introducing the idea of pre/post-conditions and loop invariants to readers of all backgrounds, as well as all the necessary mathematical foundations. The programming exercises in Python will be available on the web (see www.msoltys.com/book for the companion web site).
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Algorithms and Complexity
This book is an introductory textbook on the design and analysis of algorithms. The author uses a careful selection of a few topics to illustrate the tools for algorithm analysis. Recursive algorithms are illustrated by Quicksort, FFT, fast matrix multiplications, and others. Algorithms associated with the network flow problem are fundamental in many areas of graph connectivity, matching theory, etc. Algorithms in number theory are discussed with some applications to public key encryption. This second edition will differ from the present edition mainly in that solutions to most of the exercises will be included.
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Design and Analysis of Algorithms
The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.
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Design and Analysis of Algorithms
These are my lecture notes from CS681: Design and Analysis of Algo rithms, a one-semester graduate course I taught at Cornell for three consec utive fall semesters from '88 to '90. The course serves a dual purpose: to cover core material in algorithms for graduate students in computer science preparing for their PhD qualifying exams, and to introduce theory students to some advanced topics in the design and analysis of algorithms. The material is thus a mixture of core and advanced topics. At first I meant these notes to supplement and not supplant a textbook, but over the three years they gradually took on a life of their own. In addition to the notes, I depended heavily on the texts • A. V. Aho, J. E. Hopcroft, and J. D. Ullman, The Design and Analysis of Computer Algorithms. Addison-Wesley, 1975. • M. R. Garey and D. S. Johnson, Computers and Intractibility: A Guide to the Theory of NP-Completeness. w. H. Freeman, 1979. • R. E. Tarjan, Data Structures and Network Algorithms. SIAM Regional Conference Series in Applied Mathematics 44, 1983. and still recommend them as excellent references.
R 2,504.84
Algebraic Graph Algorithms
This textbook discusses the design and implementation of basic algebraic graph algorithms, and algebraic graph algorithms for complex networks, employing matroids whenever possible. The text describes the design of a simple parallel matrix algorithm kernel that can be used for parallel processing of algebraic graph algorithms. Example code is presented in pseudocode, together with case studies in Python and MPI. The text assumes readers have a background in graph theory and/or graph algorithms.
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Designing Hexagonal Architecture with Java
Learn how to build robust, change-tolerable, and highly maintainable cloud-native Java applications with Hexagonal Architecture and Quarkus.Key FeaturesUse hexagonal architecture to increase maintainability and reduce technical debtLearn how to build systems that are simple to change and understandLeverage Quarkus to create modern cloud-native applicationsBook DescriptionWe live in a fast-evolving world with new technologies emerging every day, where enterprises are constantly changing in an unending quest to be more profitable. So how to develop software capable of handling such a high level of unpredictability? With this question in mind, this book explores how hexagonal architecture can help build robust, change-tolerable, maintainable, and cloud-native applications that can meet the needs of enterprises seeking to increase their profits while dealing with uncertainties. This book starts covering hexagonal architecture's building blocks, such as entities, use cases, ports, and adapters. You'll learn to assemble business code in the Domain hexagon, create features with ports and use cases in the Application hexagon, and make your software compatible with different technologies by employing adapters in the Framework hexagon. In this new edition, you will learn the differences between hexagonal and layered architecture and how to apply SOLID principles while developing a hexagonal system based on a real-world scenario. Finally, you'll get to grips with using Quarkus to turn your hexagonal application into a cloud-native system. By the end of this book, you'll be able to develop robust, flexible, and maintainable systems that will stand the test of time.What you will learnLearn how to apply SOLID principles with hexagonal architectureFind out how to assemble business rules algorithms using the specification design patternCombine domain-driven design techniques with hexagonal principles to create powerful domain modelsEmploy adapters to make the system support different protocols such as REST, gRPC, and WebSocketCreate a module and package structure based on hexagonal principlesUse Java modules to enforce dependency inversion and ensure isolation between software componentsImplement Quarkus DI to manage the life cycle of input and output portsWho this book is forThis book is for software architects and Java developers who want to improve code maintainability and enhance productivity with an architecture that allows changes in technology without compromising business logic, which is precisely what hexagonal architecture does. Intermediate knowledge of the Java programming language and familiarity with Jakarta EE will help you to get the most out of this book.
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Algorithms and Computation
This book constitutes the refereed proceedings of the 26th InternationalSymposium on Algorithms and Computation, ISAAC 2015, held in Nagoya, Japan, inDecember 2015.The 65 revised full papers presented together with 3 invited talks were carefullyreviewed and selected from 180 submissions for inclusion in the book. The focusof the volume is on the following topics: computational geometry; datastructures; combinatorial optimization and approximation algorithms; randomizedalgorithms; graph algorithms and FPT; computational complexity; graph drawingand planar graphs; online and streaming algorithms; and string and DNAalgorithms.
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