Search results for "data structures and algorithms in java"
Items we need to order for you
New Data Structures and Algorithms for Logic Synthesis and Verification
This book introduces new logic primitives for electronic design automation tools. The author approaches fundamental EDA problems from a different, unconventional perspective, in order to demonstrate the key role of rethinking EDA solutions in overcoming technological limitations of present and future technologies. The author discusses techniques that improve the efficiency of logic representation, manipulation and optimization tasks by taking advantage of majority and biconditional logic primitives. Readers will be enabled to accelerate formal methods by studying core properties of logic circuits and developing new frameworks for logic reasoning engines.
R 1,903.71
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.
R 2,954.81
Grokking Data Structures
Don’t be perplexed by data structures! This fun, friendly, and fully illustrated guide makes it easy to learn useful data structures you'll put to work every day. Grokking Data Structures makes it a breeze to learn the most useful day-to-day data structures. You'll follow a steady learning path from absolute basics to advanced concepts, all illustrated with fun examples, engaging industry stories, and hundreds of graphics and cartoons. In Grokking Data Structures you'll learn how to: Understand the most important and widely used data structuresIdentify use cases where data structures make the biggest differencePick the best data structure solution for a coding challengeUnderstand the tradeoffs of data structures and avoid catastrophesImplement basic data collections like arrays, linked lists, stacks, and priority queuesUse trees and binary search trees (BSTs) to organize dataUse graphs to model relationships and learn about complex dataEfficiently search by key using hash tables and hashing functionsReason about time and memory requirements of operations on data structures Grokking Data Structures carefully guides you from the most basic data structures like arrays or linked lists all the way to powerful structures like graphs. It’s perfect for beginners, and you won't need anything more than high school math to get started. Each data structure you encounter comes with its own complete Python implementation so you can start experimenting with what you learn right away. About the technology Data structures are vital for shaping and handling your data organization. They're also an important part of most IT job interviews! Whether you're new to data structures or just dusting off what you learned in school, this book will get you up to speed fast with no advanced math, abstract theory, or complicated proofs.
R 1,628.00
Genetic Programming and Data Structures
Computers that `program themselves' has long been an aim of computer scientists. Recently genetic programming (GP) has started to show its promise by automatically evolving programs. Indeed in a small number of problems GP has evolved programs whose performance is similar to or even slightly better than that of programs written by people. The main thrust of GP has been to automatically create functions. While these can be of great use they contain no memory and relatively little work has addressed automatic creation of program code including stored data. This issue is the main focus of Genetic Programming, and Data Structures: Genetic Programming + Data Structures = Automatic Programming!. This book is motivated by the observation from software engineering that data abstraction (e.g., via abstract data types) is essential in programs created by human programmers. This book shows that abstract data types can be similarly beneficial to the automatic production of programs using GP. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! shows how abstract data types (stacks, queues and lists) can be evolved using genetic programming, demonstrates how GP can evolve general programs which solve the nested brackets problem, recognises a Dyck context free language, and implements a simple four function calculator. In these cases, an appropriate data structure is beneficial compared to simple indexed memory. This book also includes a survey of GP, with a critical review of experiments with evolving memory, and reports investigations of real world electrical network maintenance scheduling problems that demonstrate that Genetic Algorithms can find low cost viable solutions to such problems. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! should be of direct interest to computer scientists doing research on genetic programming, genetic algorithms, data structures, and artificial intelligence. In addition, this book will be of interest to practitioners working in all of these areas and to those interested in automatic programming.
R 4,653.40
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
R 1,824.00
Algorithms and Discrete Applied Mathematics
This book collects the refereed proceedingsof the Second International Conference on Algorithms and Discrete AppliedMathematics, CALDAM 2016, held in Thiruvananthapuram, India, in February 2016.The volume contains 30 full revised papers from 90 submissions along with 1invited talk presented at the conference. The conference focuses on topicsrelated to efficient algorithms and data structures, their analysis (boththeoretical and experimental) and the mathematical problems arising thereof,and new applications of discrete mathematics, advances in existing applicationsand development of new tools for discrete mathematics.
R 2,187.26
Data Structures of Pascal, Algol 68, PL/1 and Ada
This book is intended as a text for a course in programming languages. The pre requisites for such a course are insight in structured programming and knowledge as well as practical experience of at least one (e.g., Pascal) of the programming languages treated in the book. The emphasis is on language concepts rather than on syntactic details. The book covers a number of important language concepts that are related to data struc tures. The comparison of the programming languages Pascal, Algol 68, PL/1 and Ada consists in investigating how these concepts are supported by each of these languages. Interesting evaluation criteria are generality, simplicity, safety, readability and portability. The study of programming languages is based on a simple model called SMALL. This model serves as a didactic vehicle for describing, comparing and evaluating data structures in various programming languages. Each chapter centers around a specific language concept. It consists of a general discussion followed by a number of language sections, one for each of the languages Pascal, Algol 68, PL/1 and Ada. Each of these sections contains a number of illustrating program fragments written in the programming language concerned. For each program fragment in one language, there is an analogous fragment in the others. The book can be read "vertically" so that the programming languages Pascal, Algol 68, PL/1 and Ada are encountered in that order several times. A "horizontal" reading of the book would consist in selecting only those sections which only concern one language.
R 3,196.54
R 580.00
R 1,627.00
Data Structures using C
The data structure is a set of specially organized data elements and functions, which are defined to store, retrieve, remove and search for individual data elements. Data Structures using C: A Practical Approach for Beginners covers all issues related to the amount of storage needed, the amount of time required to process the data, data representation of the primary memory and operations carried out with such data. Data Structures using C: A Practical Approach for Beginners book will help students learn data structure and algorithms in a focused way. Resolves linear and nonlinear data structures in C language using the algorithm, diagrammatically and its time and space complexity analysis Covers interview questions and MCQs on all topics of campus readiness Identifies possible solutions to each problem Includes real-life and computational applications of linear and nonlinear data structuresThis book is primarily aimed at undergraduates and graduates of computer science and information technology. Students of all engineering disciplines will also find this book useful.
R 3,166.88
Algorithms and Data Structures - Applications to Graphics and Geometry
This is a college-level introductory textbook of algorithms and data structures with application to graphics and geometry. This textbook, released under a Creative Commons Share Alike (CC BY SA) license, is presented in its original format with the academic content unchanged. It was authored by Jurg Nievergelt (ETH Zurich) and Klaus Hinrichs (Institut fur Informatik) and provided by the University of Georgia's Global Textbook Project. Textbookequity.org/algorithms-and-data-structures/ Photo Credit: Renato Keshet (GFDL) commons.wikimedia.org Contents Part I: Programming environments for motion, graphics, and geometry Part II: Programming concepts: beyond notation Part IV: Complexity of problems and algorithms Part V: Data structures Textbook Equity Edition http://textbookequity.org/algorithms-and-data-structures
R 1,617.51
Algorithms
Algorithms play a central role both in the theory and in the practice of computing. The goal of the authors was to write a textbook that would not trivialize the subject but would still be readable by most students on their own. The book contains over 120 exercises. Some of them are drills; others make important points about the material covered in the text or introduce new algorithms not covered there. The book also provides programming projects. From the Table of Contents: Chapter 1: Basic knowledge of Mathematics, Relations, Recurrence relation and Solution techniques, Function and Growth of functions. Chapter 2: Different Sorting Techniques and their analysis. Chapter 3: Greedy approach, Dynamic Programming, Branch and Bound techniques, Backtracking and Problems, Amortized analysis, and Order Statics. Chapter 4: Graph algorithms, BFS, DFS, Spanning Tree, Flow Maximization Algorithms. Shortest Path Algorithms. Chapter 5: Binary search tree, Red black Tree, Binomial heap, B-Tree and Fibonacci Heap. Chapter 6: Approximation Algorithms, Sorting Networks, Matrix operations, Fast Fourier Transformation, Number theoretic Algorithm, Computational geometry Randomized Algorithms, String matching, NP-Hard, NP-Completeness, Cooks theorem.
R 1,687.00
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).
R 2,604.00
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.
R 3,081.70
R 949.54
Genetic Programming and Data Structures
Computers that `program themselves' has long been an aim of computer scientists. Recently genetic programming (GP) has started to show its promise by automatically evolving programs. Indeed in a small number of problems GP has evolved programs whose performance is similar to or even slightly better than that of programs written by people. The main thrust of GP has been to automatically create functions. While these can be of great use they contain no memory and relatively little work has addressed automatic creation of program code including stored data. This issue is the main focus of Genetic Programming, and Data Structures: Genetic Programming + Data Structures = Automatic Programming!. This book is motivated by the observation from software engineering that data abstraction (e.g., via abstract data types) is essential in programs created by human programmers. This book shows that abstract data types can be similarly beneficial to the automatic production of programs using GP. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! shows how abstract data types (stacks, queues and lists) can be evolved using genetic programming, demonstrates how GP can evolve general programs which solve the nested brackets problem, recognises a Dyck context free language, and implements a simple four function calculator. In these cases, an appropriate data structure is beneficial compared to simple indexed memory. This book also includes a survey of GP, with a critical review of experiments with evolving memory, and reports investigations of real world electrical network maintenance scheduling problems that demonstrate that Genetic Algorithms can find low cost viable solutions to such problems. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! should be of direct interest to computer scientists doing research on genetic programming, genetic algorithms, data structures, and artificial intelligence. In addition, this book will be of interest to practitioners working in all of these areas and to those interested in automatic programming.
R 4,653.57
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.
R 2,470.44
Algorithmic Aspects in Information and Management
This volume constitutes the proceedings of the 12th International Conference on Algorithmic Aspects in Information and Management, AAIM 2018, held in Dallas, Texas, USA, in December 2018.The 25 full papers presented were carefully reviewed and selected. The papers deal with current trends of research on algorithms, data structures, operation research, combinatorial optimization and their applications.
R 2,178.46
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).
R 1,953.00
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.
R 4,288.61
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.
R 1,560.93
Basic Concepts In Algorithms
This book is the result of several decades of teaching experience in data structures and algorithms. It is self-contained but does assume some prior knowledge of data structures, and a grasp of basic programming and mathematics tools. Basic Concepts in Algorithms focuses on more advanced paradigms and methods combining basic programming constructs as building blocks and their usefulness in the derivation of algorithms. Its coverage includes the algorithms' design process and an analysis of their performance. It is primarily intended as a textbook for the teaching of Algorithms for second year undergraduate students in study fields related to computers and programming.Klein reproduces his oral teaching style in writing, with one topic leading to another, related one. Most of the classical and some more advanced subjects in the theory of algorithms are covered, though not in a comprehensive manner. The topics include Divide and Conquer, Dynamic Programming, Graph algorithms, probabilistic algorithms, data compression, numerical algorithms and intractability. Each chapter comes with its own set of exercises, and solutions to most of them are appended.Related Link(s)
R 5,053.96
C++ Data Structures: A Laboratory Course
C++ Data Structures: A Laboratory Course exemplifies the active learning experience. With a dynamic learn-by-doing focus, this laboratory manual encourages students to explore data structures by implementing them, a process through which students discover how data structures work and how they can be applied. Providing a framework that offers feedback and support, this text challenges students to exercise their creativity in both programming and analysis. Topics covered include: Text ADT, BlogEntry ADT, Stack ADT, Heap ADT, Weighted Graph ADT, and much more!
R 3,206.00
Machine Vision Algorithms in Java
This book presents key machine vision techniques and algorithms, along with the associated Java source code. Special features include a complete self-contained treatment of all topics and techniques essential to the understanding and implementation of machine vision; an introduction to object-oriented programming and to the Java programming language, with particular reference to its imaging capabilities; Java source code for a wide range of real-world image processing and analysis functions; an introduction to the Java 2D imaging and Java Advanced Imaging (JAI) API; and a wide range of illustrative examples.
R 4,654.89
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.
R 4,649.45
Algorithms and Computation
This book constitutes the refereed proceedings of the 25th International Symposium on Algorithms and Computation, ISAAC 2014, held in Jeonju, Korea, in December 2014. The 60 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 171 submissions for inclusion in the book. The focus of the volume in on the following topics: computational geometry, combinatorial optimization, graph algorithms: enumeration, matching and assignment, data structures and algorithms, fixed-parameter tractable algorithms, scheduling algorithms, computational complexity, computational complexity, approximation algorithms, graph theory and algorithms, online and approximation algorithms, and network and scheduling algorithms.
R 4,354.20
Data Structures for Engineers and Scientists Using Python
The text covers the fundamentals of Python programming and the implementation of data structures using Python programming with the help of worked-out examples. It provides a learning tool for engineers as well as for researchers and scientists of advanced level. The text further discusses important concepts such as polynomial manipulation, sparse matrices, implementation of stack using the queue model and topological sorting.This book:Discusses the implementation of various data structures such as an array, stack, queue, tree and graph along with sorting and searching algorithms. Includes programming tips to highlight important concepts and help readers avoid common programming errors. Presents each concept of data structure with a different approach and implements the same using Python programming. Offers rich chapter-end pedagogy including objective-type questions (with answers), review questions and programming exercises to facilitate review. Covers fundamentals of Python up to object-oriented concepts including regular expression. It is primarily written for senior undergraduate, graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering and information technology.
R 3,297.00
R 1,067.00
Java and Object Orientation: An Introduction
This second edition shows readers how to build object oriented applications in Java. Written in a clear and concise style, with lots of examples, this revised edition provides: a detailed understanding of object orientation, a thorough introduction to Java including building blocks, constructs, classes, data structures etc, coverage of graphical user interfaces and applets (AWT; Servlets), and object oriented analysis. If you are looking for a good introduction to Java and object orientation, then this is the book for you. Source code for the examples in this book is available on the Internet.
R 1,768.40
Object-Orientation, Abstraction, and Data Structures Using Scala
Praise for the first edition:"The well-written, comprehensive book…[is] aiming to become a de facto reference for the language and its features and capabilities. The pace is appropriate for beginners; programming concepts are introduced progressively through a range of examples and then used as tools for building applications in various domains, including sophisticated data structures and algorithms…Highly recommended. Students of all levels, faculty, and professionals/practitioners. —D. Papamichail, University of Miami in CHOICE MagazineMark Lewis’ Introduction to the Art of Programming Using Scala was the first textbook to use Scala for introductory CS courses. Fully revised and expanded, the new edition of this popular text has been divided into two books. Object-Orientation, Abstraction, and Data Structures Using Scala, Second Edition is intended to be used as a textbook for a second or third semester course in Computer Science.The Scala programming language provides powerful constructs for expressing both object orientation and abstraction. This book provides students with these tools of object orientation to help them structure solutions to larger, more complex problems, and to expand on their knowledge of abstraction so that they can make their code more powerful and flexible. The book also illustrates key concepts through the creation of data structures, showing how data structures can be written, and the strengths and weaknesses of each one. Libraries that provide the functionality needed to do real programming are also explored in the text, including GUIs, multithreading, and networking. The book is filled with end-of-chapter projects and exercises, and the authors have also posted a number of different supplements on the book website. Video lectures for each chapter in the book are also available on YouTube. The videos show construction of code from the ground up and this type of "live coding" is invaluable for learning to program, as it allows students into the mind of a more experienced programmer, where they can see the thought processes associated with the development of the code. About the AuthorsMark Lewis is an Associate Professor at Trinity University. He teaches a number of different courses, spanning from first semester introductory courses to advanced seminars. His research interests included simulations and modeling, programming languages, and numerical modeling of rings around planets with nearby moons. Lisa Lacher is an Assistant Professor at the University of Houston, Clear Lake with over 25 years of professional software development experience. She teaches a number of different courses spanning from first semester introductory courses to graduate level courses. Her research interests include Computer Science Education, Agile Software Development, Human Computer Interaction and Usability Engineering, as well as Measurement and Empirical Software Engineering.
R 5,962.34
Implicit Curves and Surfaces: Mathematics, Data Structures and Algorithms
Implicit objects have gained increasing importance in geometric modeling, visualisation, animation, and computer graphics, because their geometric properties provide a good alternative to traditional parametric objects. This book presents the mathematics, computational methods and data structures, as well as the algorithms needed to render implicit curves and surfaces, and shows how implicit objects can easily describe smooth, intricate, and articulatable shapes, and hence why they are being increasingly used in graphical applications. Divided into two parts, the first introduces the mathematics of implicit curves and surfaces, as well as the data structures suited to store their sampled or discrete approximations, and the second deals with different computational methods for sampling implicit curves and surfaces, with particular reference to how these are applied to functions in 2D and 3D spaces.
R 3,950.94
R 341.00
Algorithms and Complexity
The papers in this volume were presented at the 5th Italian Conference on AlgorithmsandComplexity(CIAC2003). Theconferencetookplaceduring May 28-30, 2003, in Rome, Italy, at the Conference Centre of the University of Rome "La Sapienza. " CIAC started in 1990 as a national meeting to be held every three years for Italian researchers in algorithms, data structures, complexity theory, and parallelanddistributedcomputing. Duetoasigni?cantparticipationofforeign researchers, starting from the second edition, CIAC evolved into an international conference. However,alltheeditionsofCIAChavebeenheldinRome. The proceedings of CIAC were published by World Scienti?c for the ?rst edition and by Springer-Verlag in the Lecture Notes in Computer Science series (volumes 778,1203and1767)forthesubsequenteditions. Aselectionofthepapersofthe fourth edition was published in a special issue of Theoretical Computer Science Vol. 285(1),2002. Thisyearweexpecttopublishanextendedversionofselected papers presented at the conference in a special issue of the journal Theory of Computing Systems.In response to the call for papers for CIAC 2003, 57 papers were subm- ted, from which the Program Committee selected 23 papers for presentation at theconferencefrom18countries. Eachpaperwasevaluatedbyatleastthree Program Committee members with the help of 63 external reviewers. In addition to the selected papers, the Organizing Committee invited CharlesE. Leiserson(Cambridge),DavidPeleg(Rehovot),MichaelO. Rabin (CambridgeandJerusalem),JohnE. Savage(Providence),andLucaTrevisan (Berkeley)togiveplenarylecturesattheconference. Moreover,threetutorials byDavidPeleg,JaymeL. Szwarc?ter(RiodeJaneiro)andLucaTrevisanwere o?ered in the days preceding the conference. We wish to express our appreciation to all the authors of the submitted - pers, to the Program Committee members and the referees, to the Organizing Committee, and to the plenary and tutorial lecturers who accepted our in- tation.
R 2,015.00
R 393.25
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.
R 3,153.71
