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Algorithms - ESA 2001
This book constitutes the refereed proceedings of the 9th Annual European Symposium on Algorithms, ESA 2001, held in Aarhus, Denmark, in August 2001.The 41 revised full papers presented together with three invited contributions were carefully reviewed and selected from 102 submissions. The papers are organized in topical sections on caching and prefetching, online algorithms, data structures, optimization and approximation, sequences, scheduling, shortest paths, geometry, distributed algorithms, graph algorithms, pricing, broadcasting and multicasting, graph labeling and graph drawing, and graphs.
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Algorithms on Trees and Graphs
Graph algorithms is a well-established subject in mathematics and computer science. Beyond classical application fields, such as approximation, combinatorial optimization, graphics, and operations research, graph algorithms have recently attracted increased attention from computational molecular biology and computational chemistry. Centered around the fundamental issue of graph isomorphism, this text goes beyond classical graph problems of shortest paths, spanning trees, flows in networks, and matchings in bipartite graphs. Advanced algorithmic results and techniques of practical relevance are presented in a coherent and consolidated way. This book introduces graph algorithms on an intuitive basis followed by a detailed exposition in a literate programming style, with correctness proofs as well as worst-case analyses. Furthermore, full C++ implementations of all algorithms presented are given using the LEDA library of efficient data structures and algorithms.
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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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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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Data Science and Algorithms in Systems
This book offers real-world data science and algorithm design topics linked to systems and software engineering. Furthermore, articles describing unique techniques in data science, algorithm design, and systems and software engineering are featured.This book is the second part of the refereed proceedings of the 6th Computational Methods in Systems and Software 2022 (CoMeSySo 2022). The CoMeSySo 2022 conference, which is being hosted online, is breaking down barriers. CoMeSySo 2022 aims to provide a worldwide venue for debate of the most recent high-quality research findings.
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Algorithms: Design Techniques And Analysis
Problem solving is an essential part of every scientific discipline. It has two components: (1) problem identification and formulation, and (2) the solution to the formulated problem. One can solve a problem on its own using ad hoc techniques or by following techniques that have produced efficient solutions to similar problems. This required the understanding of various algorithm design techniques, how and when to use them to formulate solutions, and the context appropriate for each of them.This book presents a design thinking approach to problem solving in computing — by first using algorithmic analysis to study the specifications of the problem, before mapping the problem on to data structures, then on to the situatable algorithms. Each technique or strategy is covered in its own chapter supported by numerous examples of problems and their algorithms. The new edition includes a comprehensive chapter on parallel algorithms, and many enhancements.
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Data structures based on linear relations
Data structures is a key course for computer science and related majors. This book presents a variety of practical or engineering cases and derives abstract concepts from concrete problems. Besides basic concepts and analysis methods, it introduces basic data types such as sequential list, tree as well as graph. This book can be used as an undergraduate textbook, as a training textbook or a self-study textbook for engineers.
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Automated Verification of Concurrent Search Structures
Search structures support the fundamental data storage primitives on key-value pairs: insert a pair, delete by key, search by key, and update the value associated with a key. Concurrent search structures are parallel algorithms to speed access to search structures on multicore and distributed servers. These sophisticated algorithms perform fine-grained synchronization between threads, making them notoriously difficult to design correctly. Indeed, bugs have been found both in actual implementations and in the designs proposed by experts in peer-reviewed publications. The rapid development and deployment of these concurrent algorithms has resulted in a rift between the algorithms that can be verified by the state-of-the-art techniques and those being developed and used today. The goal of this book is to show how to bridge this gap in order to bring the certified safety of formal verification to high-performance concurrent search structures. Similar techniques and frameworks can be applied to concurrent graph and network algorithms beyond search structures.
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Algorithms in Action
Algorithms in Action effectively introduces students to a variety of techniques for designing algorithms with a focus on developing intuitive understanding. Readers learn how to successfully construct foundational algorithms, preparing them for more advanced courses in the discipline, as well as professional application.Over the course of nine chapters, students learn fundamental concepts critical to the development of algorithms, paired with detailed visual representations that walk readers step-by-step through algorithm execution. The text begins with a review of runtime complexity, lower bound for sorting, and trees and graphs, then moves into more complex topical areas, including amortized analysis, heaps, dynamic programming, network flow, linear programming, and NP-completeness. The book includes over 160 figures, as well as review questions and exercises at the end of each chapter, to encourage learning, retention, practice, and application.The revised first edition features corrections throughout the text, as well as an updated preface that underscores the book's value and outlines the ways in which it fits within a graduate student's course progression. Developed to provide students with an approachable and effective introduction to algorithm design,Algorithms in Action is an ideal resource for advanced undergraduate or master-level courses in computer science or related technical disciplines. Foundational knowledge of discrete mathematics, data structures, and calculus is recommended as a prerequisite.
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Efficient Algorithm Design
Master advanced algorithm design techniques to tackle complex programming challenges and optimize application performanceKey FeaturesDevelop advanced algorithm design skills to solve modern computational problemsLearn state-of-the-art techniques to deepen your understanding of complex algorithmsApply your skills to real-world scenarios, enhancing your expertise in today's tech landscapePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionEfficient Algorithm Design redefines algorithms, tracing the evolution of computer science as a discipline bridging natural science and mathematics. Author Masoud Makrehchi, PhD, with his extensive experience in delivering publications and presentations, explores the duality of computers as mortal hardware and immortal algorithms. The book guides you through essential aspects of algorithm design and analysis, including proving correctness and the importance of repetition and loops. This groundwork sets the stage for exploring algorithm complexity, with practical exercises in design and analysis using sorting and search as examples. Each chapter delves into critical topics such as recursion and dynamic programming, reinforced with practical examples and exercises that link theory with real-world applications. What sets this book apart is its focus on the practical application of algorithm design and analysis, equipping you to solve real programming challenges effectively.By the end of this book, you’ll have a deep understanding of algorithmic foundations and gain proficiency in designing efficient algorithms, empowering you to develop more robust and optimized software solutions.What you will learnGain skills in advanced algorithm design for better problem-solvingUnderstand algorithm correctness and complexity for robust softwareApply theoretical concepts to real-world scenarios for practical solutionsMaster sorting and search algorithms, understanding their synergyExplore recursion and recurrence for complex algorithmic structuresLeverage dynamic programming to optimize algorithmsGrasp the impact of data structures on algorithm efficiency and designWho this book is forIf you’re a software engineer, computer scientist, or a student in a related field looking to deepen your understanding of algorithm design and analysis, this book is tailored for you. A foundation in programming and a grasp of basic mathematical concepts is recommended. It's an ideal resource for those already familiar with the basics of algorithms who want to explore more advanced topics. Data scientists and AI developers will find this book invaluable for enhancing their algorithmic approaches in practical applications.
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Data Structures and Program Design Using C++
Data structures provide a means to managing large amounts of information such as large databases, using SEO effectively, and creating Internet/Web indexing services. This book is designed to present fundamentals of data structures for beginners using the C++ programming language in a friendly, self-teaching, format. Practical analogies using real world applications are integrated throughout the text to explain technical concepts. The book includes a variety of end-of-chapter practice exercises, e.g. programming, theoretical, and multiple-choice.Features:Covers data structure fundamentals using C++Numerous tips, analogies, and practical applications enhance understanding of subjects under discussion"Frequently Asked Questions" integrated throughout the text clarify and explain conceptsIncludes a variety of end-of-chapter exercises, e.g., programming, theoretical, and multiple choice
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Python Data Structures Pocket Primer
As part of the best-selling Pocket Primer series, this book is designed to present the fundamentals of data structures using Python. Data structures provide a means to managing huge amounts of information such as large databases and the ability to use search and sort algorithms effectively. It is intended to be a fast-paced introduction to the core concepts of Python and data structures, illustrated with numerous code samples. Companion files with source code are available for downloading.
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WALCOM: Algorithms and Computation
This book constitutes the proceedings of the 13th International Conference and Workshop on Algorithms and Computation, WALCOM 2019, held in Guwahati, India, in February/ March 2019.The 30 full papers presented were carefully reviewed and selected from 100 submissions. The papers are organized in topical headings on the facility location problem; computational geometry; graph drawing; graph algorithms; approximation algorithms; miscellaneous; data structures; parallel and distributed algorithms; and packing and covering.
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Algorithms And Complexity - Proceedings Of The First Italian Conference
This proceedings contains contributions on topics such as the models of computation, analysis and design of sequential and parallel algorithms, data structures and their applications, approximating algorithms and probabilistic analysis, and computational complexity.
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Algorithms and Complexity
Here are the refereed proceedings of the 6th Italian Conference on Algorithms and Computation, CIAC 2006. The 33 revised full papers presented together with 3 invited papers address such topics as sequential, parallel and distributed algorithms, data structures, approximation algorithms, randomized algorithms, on-line algorithms, graph algorithms, analysis of algorithms, algorithm engineering, algorithmic game theory, computational biology, computational complexity, communication networks, computational geometry, cryptography, discrete optimization, graph drawing, mathematical programming, and quantum algorithms.
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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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Nature-Inspired Algorithms for Big Data Frameworks
As technology continues to become more sophisticated, mimicking natural processes and phenomena becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Algorithms for Big Data Frameworks is a collection of innovative research on the methods and applications of extracting meaningful information from data using algorithms that are capable of handling the constraints of processing time, memory usage, and the dynamic and unstructured nature of data. Highlighting a range of topics including genetic algorithms, data classification, and wireless sensor networks, this book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the application of nature and biologically inspired algorithms for handling challenges posed by big data in diverse environments.
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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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Algorithms
This book confronts head-on the main challenges students face in algorithm design and analysis. While maintaining rigor, it breaks down some of the most difficult aspects of algorithm design into step-by-step procedures that all students can follow. In the discussion of basic algorithm design paradigms—graph search, reductions to solved problems, greedy algorithms, divide-and conquer, backtracking, dynamic programming, and gradient descent or hill-climbing—we emphasize not only the techniques themselves but also how to think when designing algorithms for new problems. The book also provides templates for correctness proofs that are tailored to each design paradigm. A wide range of examples are included for every paradigm, from canonical algorithms that clearly illustrate the core ideas to examples that stretch the paradigm in different ways. Within the existing toolbox developed by algorithms researchers, there are both standard tools that students should master and more surprising results that even their discoverers found remarkable. We present both, taking care to distinguish between them and to convey their different roles in algorithmic problem solving. The book includes many types of assignments designed to keep students engaged with the subject. Comprehension quizzes allow students to self-test their understanding of basic vocabulary and concepts. Algorithm design problems range from guided exercises, which walk students through the procedure for designing an algorithm, to open-ended problems that admit multiple correct approaches. Empirical experimental problems give students hands-on experience implementing algorithms (in a language of their choice), testing them experimentally, and summarizing collected data. These assignments also help students better understand both the significance and the limitations of asymptotic analysis, including the differences between worst-case, average-case, and typical algorithm performance. The book was developed for an upper-division undergraduate algorithms course, but it can also be used for a lower-division class (starting with the appendices) or an introductory graduate course (including the optional advanced sections).
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Optimal Algorithms
This volume brings together papers from various fields of theoretical computer science, including computational geometry, parallel algorithms, algorithms on graphs, data structures and complexity of algorithms. Some of the invited papers include surveys of results in particular fields and some report original research, while all the contributed papers report original research. Most of the algorithms given are for parallel models of computation. The papers were presented at the Second International Symposium on Optimal Algorithms held in Varna, Bulgaria, in May/June 1989. The volume will be useful to researchers and students in theoretical computer science, especially in parallel computing.
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Algorithmen und Datenstrukturen im VLSI-Design
Eines der Hauptprobleme beim Chipentwurf besteht darin, daß die Anzahl der zu bewältigenden Kombinationen der einzelnen Chipbausteine ins Unermeßliche steigt. Hier hat sich eine sehr fruchtbare Verbindung zu einem Kerngebiet der Theoretischen Informatik, dem Gebiet des Entwurfs von Datenstrukturen und effizienten Algorithmen, herstellen lassen: das Konzept der geordneten binären Entscheidungsgraphen, das in zahlreichen CAD-Projekten zu einer beträchtlichen Leistungssteigerung geführt hat. Die Autoren stellen die Grundlagen dieses interdisziplinären Forschungsgebiets dar und behandeln wichtige Anwendungen aus dem rechnergestützten Schaltkreisentwurf.
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Algorithms on Trees and Graphs
Graph algorithms is a well-established subject in mathematics and computer science. Beyond classical application fields, such as approximation, combinatorial optimization, graphics, and operations research, graph algorithms have recently attracted increased attention from computational molecular biology and computational chemistry. Centered around the fundamental issue of graph isomorphism, this text goes beyond classical graph problems of shortest paths, spanning trees, flows in networks, and matchings in bipartite graphs. Advanced algorithmic results and techniques of practical relevance are presented in a coherent and consolidated way. This book introduces graph algorithms on an intuitive basis followed by a detailed exposition in a literate programming style, with correctness proofs as well as worst-case analyses. Furthermore, full C++ implementations of all algorithms presented are given using the LEDA library of efficient data structures and algorithms.
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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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Advances in Computational Algorithms and Data Analysis
Advances in Computational Algorithms and Data Analysis offers state of the art tremendous advances in computational algorithms and data analysis. The selected articles are representative in these subjects sitting on the top-end-high technologies. The volume serves as an excellent reference work for researchers and graduate students working on computational algorithms and data analysis.
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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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WALCOM: Algorithms and Computation
This book constitutes the proceedings of the 10th International Workshop on Algorithms and Computation, WALCOM 2016, held in Kathmandu, Nepal, in March 2016.The 27 full papers presented together with 4 invited talks were carefully reviewed and selected from 68 submissions. The papers cover a wide range of topics such as approximation algorithms, computational complexity, computational geometry, data structures, graph algorithms, graph coloring, graph exploration, and online algorithms.
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Learning Algorithms
When it comes to writing efficient code, every software professional needs to have an effective working knowledge of algorithms. In this practical book, author George Heineman (Algorithms in a Nutshell) provides concise and informative descriptions of key algorithms that improve coding. Software developers, testers, and maintainers will discover how algorithms solve computational problems creatively.Each chapter builds on earlier chapters through eye-catching visuals and a steady rollout of essential concepts, including an algorithm analysis to classify the performance of every algorithm presented in the book. At the end of each chapter, you'll get to apply what you've learned to a novel challenge problem -- simulating the experience you might find in a technical code interview.With this book, you will:Examine fundamental algorithms central to computer science and software engineeringLearn common strategies for efficient problem solving -- such as divide and conquer, dynamic programming, and greedy approachesAnalyze code to evaluate time complexity using big O notationUse existing Python libraries and data structures to solve problems using algorithmsUnderstand the main steps of important algorithms
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Data Structures and Program Design Using C
Data structures provide a means to manage large amounts of information such as large databases, using SEO, and creating Internet/Web indexing services. The book is designed to present fundamentals of data structures for beginners using the C programming language. Practical analogies using real world applications are integrated throughout the text to explain the technical concepts presented. Features:Covers data structure fundamentals using CNumerous tips and practical applications enhance understanding of concepts
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WALCOM: Algorithms and Computation
This book constitutes the refereed proceedings of the 14th International Conference on Algorithms and Computation, WALCOM 2020, held in Singapore in March/April 2020. The 23 full and 4 short papers presented were carefully reviewed and selected from 66 submissions. The papers focus on algorithmic graph theory and combinatorics, computational biology, computational geometry, data structures, experimental algorithm methodologies, graph algorithms, graph drawing, parallel and distributed algorithms, network optimization.
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Topological Data Structures for Surfaces
In Geography and GIS, surfaces can be analysed and visualised through various data structures, and topological data structures describe surfaces in the form of a relationship between certain surface-specific features. Drawn from many disciplines with a strong applied aspect, this is a research-led, interdisciplinary approach to the creation, analysis and visualisation of surfaces, focussing on topological data structures. Topological Data Structures for Surfaces: an introduction for Geographical Information Science describes the concepts and applications of these data structures. The book focuses on how these data structures can be used to analyse and visualise surface datasets from a range of disciplines such as human geography, computer graphics, metrology, and physical geography. Divided into two Parts, Part I defines the topological surface data structures and explains the various automated methods used for their generation. Part II demonstrates a number of applications of surface networks in diverse fields, ranging from sub-atomic particle collision visualisation to the study of population density patterns. To ensure that the material is accessible, each Part is prefaced by an overview of the techniques and application. Provides GI scientists and geographers with an accessible overview of current surface topology research.Algorithms are presented and explained with practical examples of their usage.Features an accompanying website developed by the Editor - http://geog.le.ac.uk/sanjayrana/surface-networks/ This book is invaluable for researchers and postgraduate students working in departments of GI Science, Geography and Computer Science. It also constitutes key reference material for Masters students working on surface analysis projects as part of a GI Science or Computer Science programme.
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40 Algorithms Every Programmer Should Know
Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental algorithms, such as sorting and searching, to modern algorithms used in machine learning and cryptographyKey FeaturesLearn the techniques you need to know to design algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore different types of algorithms and choose the right data structures for their optimal implementationBook DescriptionAlgorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems 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, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. 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 book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisWork with machine learning algorithms to cluster similar tweets and process Twitter data in real timePredict the weather using supervised learning algorithmsUse neural networks for object detectionCreate a recommendation engine that suggests relevant movies to subscribersImplement foolproof security using symmetric and asymmetric encryption on Google Cloud Platform (GCP)Who this book is forThis 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 commonly used algorithms in a clear and concise way or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Although Python programming experience is a must, knowledge of data science will be helpful but not necessary.
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Elementary Approach To Design And Analysis Of Algorithms, An
'The book under review is an interesting elaboration that fills the gaps in libraries for concisely written and student-friendly books about essentials in computer science … I recommend this book for anyone who would like to study algorithms, learn a lot about computer science or simply would like to deepen their knowledge … The book is written in very simple English and can be understood even by those with limited knowledge of the English language. It should be emphasized that, despite the fact that the book consists of many examples, mathematical formulas and theorems, it is very hard to find any mistakes, errors or typos.'zbMATHIn computer science, an algorithm is an unambiguous specification of how to solve a class of problems. Algorithms can perform calculation, data processing and automated reasoning tasks.As an effective method, an algorithm can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing 'output' and terminating at a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input.This book introduces a set of concepts in solving problems computationally such as Growth of Functions; Backtracking; Divide and Conquer; Greedy Algorithms; Dynamic Programming; Elementary Graph Algorithms; Minimal Spanning Tree; Single-Source Shortest Paths; All Pairs Shortest Paths; Flow Networks; Polynomial Multiplication, to ways of solving NP-Complete Problems, supported with comprehensive, and detailed problems and solutions, making it an ideal resource to those studying computer science, computer engineering and information technology.
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Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation
This book constitutes the refereed proceedings of the 13th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2017, held in Ustroń, Poland, in May/June 2017.It consists of 44 carefully reviewed papers selected from 118 submissions. The papers are organized in topical sections, namely big data and cloud computing; artificial intelligence, data mining and knowledge discovery; architectures, structures and algorithms for efficient data processing; text mining, natural language processing, ontologies and semantic web; bioinformatics and biological data analysis; industrial applications; data mining tools, optimization and compression.
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Algorithmic Aspects in Information and Management
This book constitutes the refereed proceedings of the 4th International Conference on Algorithmic Aspects in Information and Management, AAIM 2008, held in Shanghai, China, in June 2008. The 30 revised full papers presented together with abstracts of 2 invited talks were carefully reviewed and selected from 53 submissions. The papers cover original algorithmic research on immediate applications and/or fundamental problems pertinent to information management and management science. Topics addressed are: approximation algorithms, geometric data management, biological data management, graph algorithms, computational finance, mechanism design, computational game theory, network optimization, data structures, operations research, discrete optimization, online algorithms, FPT algorithms, and scheduling algorithms.
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Analysis and Design of Algorithms
Analysis and Design of Algorithms provides a structured view of algorithm design techniques in a concise, easy-to-read manner. The book was written with an express purpose of being easy — to understand, read, and carry.It presents a pioneering approach in the teaching of algorithms, based on learning algorithm design techniques, and not merely solving a collection of problems. This allows students to master one design technique at a time and apply it to a rich variety of problems.Analysis and Design of Algorithms covers the algorithmic design techniques of divide and conquer, greedy, dynamic programming, branch and bound, and graph traversal. For each of these techniques, there are templates and guidelines on when to use and not to use each technique. Many sections contain innovative mnemonics to aid the readers in remembering the templates and key takeaways. Additionally, the book covers NP-completeness and the inherent hardness of problems. The third edition includes a new section on polynomial multiplication, as well as additional exercise problems, and an updated appendix.Written with input from students and professionals, Analysis and Design of Algorithms is well suited for introductory algorithm courses at the undergraduate and graduate levels. The structured organization of the text makes it especially appropriate for online and distance learning.
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Nature-Inspired Optimization Algorithms with Java
Gain insight into the world of nature-inspired optimization techniques and algorithms. This book will prepare you to apply different nature-inspired optimization techniques to solve problems using Java.You'll start with an introduction to the hidden algorithms that nature uses and find the approximate solutions to optimization problems. You'll then see how living creatures such as fish and birds are able to perform computation to solve specific optimization tasks. This book also covers various nature-inspired algorithms by reviewing code examples for each one followed by crisp and clear explanations of the algorithm using Java code. You'll examine the use of each algorithm in specific industry scenarios such as fleet scheduling in supply chain management, and shop floor management in industrial and manufacturing applications. Nature-Inspired Optimization Algorithms with Java is your pathway to understanding a variety of optimization problems that exist in various industries and domains and it will develop an ability to apply nature-inspired algorithms to find approximate solutions to them.What You'll LearnStudy optimization and its problemsExamine nature-inspired algorithms such as Particle Swarm, Gray wolf, etc.See how nature-inspired algorithms are being used to solve optimization problemsUse Java for solving the different nature-inspired algorithms with real-world examplesWho This Book Is For Software developers/architects who are looking to hone their skills in area of problem solving related to optimization with Java.
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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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Nature-Inspired Algorithms for Big Data Frameworks
As technology continues to become more sophisticated, mimicking natural processes and phenomena becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror the natural processes and systems that have existed for centuries.Nature-Inspired Algorithms for Big Data Frameworks is a collection of innovative research on the methods and applications of extracting meaningful information from data using algorithms that are capable of handling the constraints of processing time, memory usage, and the dynamic and unstructured nature of data. Highlighting a range of topics including genetic algorithms, data classification, and wireless sensor networks, this book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the application of nature and biologically inspired algorithms for handling challenges posed by big data in diverse environments.
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Introduction to Algorithms for Data Mining and Machine Learning
Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.
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