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R 1,221.00
R 341.00
Advanced Applications of Python Data Structures and Algorithms
Data structures are essential principles applicable to any programming language in computer science. Data structures may be studied more easily with Python than with any other programming language because of their interpretability, interactivity, and object-oriented nature. Computers may store and process data at an extraordinary rate and with outstanding accuracy. Therefore, it is of the utmost importance that the data is efficiently stored and is able to be accessed promptly. In addition, data processing should take as little time as feasible while maintaining the highest possible level of precision.Advanced Applications of Python Data Structures and Algorithms assists in understanding and applying the fundamentals of data structures and their many implementations and discusses the advantages and disadvantages of various data structures. Covering key topics such as Python, linked lists, datatypes, and operators, this reference work is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.
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Design of Dynamic Data Structures
In numerous computer applications there is a need of storing large sets of objects in such a way that some questions about those objects can be answered efficiently. Data structures that store such sets of objects can be either static (built for a fixed set of objects) or dynamic (insertions of new objects and deletions of existing objects can be performed). Especially for more complex searching problems as they arise in such fields as computational geometry, database design and computer graphics, only static data structures are available. This book aims at remedying this lack of flexibility by providing a number of general techniques for turning static data structures for searching problems into dynamic structures. Although the approach is basically theoretical, the techniques offered are often practically applicable. The book is written in such a way that it is readable for those who have some elementary knowledge of data structures and algorithms. Although this monograph was first published in 1983, it is still unique as a general treatment of methods for constructing dynamic data structures.
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Practical Data Structures and Algorithms for Java Developers
Master data structures and algorithms in Java through a practical, engineering-focused approach that goes beyond theory. With modern examples and a structured approach, this book emphasizes not only how algorithms work, but how to choose, adapt, and apply them in real-world systems.To start, you will conquer fundamental concepts such as arrays and linked lists. From there, the book progresses through essential sorting and searching techniques such as QuickSort and Binary Search. Then, you will explore the implementation and use cases of stacks and queues, followed by tree and graph structures with traversal algorithms. The book ends with you tackling advanced topics, such as dynamic programming, tries, and advanced graph algorithms.Finally, you will be ready to put your skills to the test and apply your knowledge in real-world situations for performance optimization.What You Will Learn: Implement and optimize essential data structures in JavaSolve complex algorithmic problems with confidenceMaster best practices for writing efficient, scalable Java code for enterprise applicationsWho This Book Is For: Java developers with a basic understanding of programming concepts who want to deepen their knowledge of data structures and algorithms. Secondary audiences include students, competitive programmers, and software engineers preparing for technical interviews.
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R 1,600.01
Advanced Applications of Python Data Structures and Algorithms
Data structures are essential principles applicable to any programming language in computer science. Data structures may be studied more easily with Python than with any other programming language because of their interpretability, interactivity, and object-oriented nature. Computers may store and process data at an extraordinary rate and with outstanding accuracy. Therefore, it is of the utmost importance that the data is efficiently stored and is able to be accessed promptly. In addition, data processing should take as little time as feasible while maintaining the highest possible level of precision.Advanced Applications of Python Data Structures and Algorithms assists in understanding and applying the fundamentals of data structures and their many implementations and discusses the advantages and disadvantages of various data structures. Covering key topics such as Python, linked lists, datatypes, and operators, this reference work is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.
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R 550.00
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.
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Space-Efficient Data Structures, Streams, and Algorithms
This Festschrift volume, published in honour of J. Ian Munro, contains contributions written by some of his colleagues, former students, and friends. In celebration of his 66th birthday the colloquium "Conference on Space Efficient Data Structures, Streams and Algorithms" was held in Waterloo, ON, Canada, during August 15-16, 2013. The articles presented herein cover some of the main topics of Ian's research interests. Together they give a good overall perspective of the last 40 years of research in algorithms and data structures.
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Manufacturing Data Structures
Manufacturing Data Structures "Comprehensive yeteasy-to-read.Manufacturing Data Structures is filled withanecdotes, yet stresses the importance of maintaining dataaccuracy. It is valuable reading for all manufacturing managers."Jim Carnall Manufacturing Manager, Eastman Kodak "An entertainingand informative look at an important aspect of day to day businessin the MRP II environment. It clearly shows how data structuringmethodology can be directly applied to process industries such asthe Personal Products/Health and Beauty business." Jeff L. StevensManager, Packaging Sciences, Chesebrough-Ponds Canada"Manufacturing Data Structures shows, in a very practical way, howmanufacturing data can be used as a competitive weapon. It's acomprehensive guide, filled with solutions to everyday problems."Jim Hendrickson Plant Manager, Reckitt & Colman "An excellentbook.Very useful on the subject of data foundations formanufacturing. It has suggested further opportunities forimprovement in my own organisation." R.A. Watson Rolls-Royce MotorCars "Manufacturing Data Structures will be of immense value to thepractitioner." Chris Cage ICI Pharmaceuticals
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Algorithms and Data Structures in VLSI Design
One of the main problems in chip design is the enormous number of possible combinations of individual chip elements within a system, and the problem of their compatibility. The recent application of data structures, efficient algorithms, and ordered binary decision diagrams (OBDDs) has proven vital in designing the computer chips of tomorrow. This book provides an introduction to the foundations of this interdisciplinary research area, emphasizing its applications in computer aided circuit design.
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Organic Structures from Spectra
The derivation of structural information from spectroscopic data is now an integral part of organic chemistry courses at all Universities. A critical part of any such course is a suitable set of problems to develop the students’ understanding of how organic structures are determined from spectra. The book builds on the very successful teaching philosophy of learning by hands-on problem solving; carefully graded examples build confidence and develop and consolidate a student’s understanding of organic spectroscopy. Organic Structures from Spectra, 6th Edition is a carefully chosen set of about 250 structural problems employing the major modern spectroscopic techniques, including Mass Spectrometry, 1D and 2D 13C and 1H NMR Spectroscopy and Infrared Spectroscopy. There are 25 problems specifically dealing with the interpretation of spin–spin coupling in proton NMR spectra and 10 problems based on the quantitative analysis of mixtures using proton and carbon NMR spectroscopy. The accompanying text is descriptive and only explains the underlying theory at a level that is sufficient to tackle the problems. The text includes condensed tables of characteristic spectral properties covering the frequently encountered functional groups. The examples themselves have been selected to include all important structural features and to emphasise connectivity arguments and stereochemistry. Many of the compounds were synthesised specifically for this book. In this collection, there are many additional easy problems designed to build confidence and to demonstrate basic principles. The Sixth Edition of this popular textbook: now incorporates many new problems using 2D NMR spectra (C–H Correlation spectroscopy, HMBC, COSY, NOESY and TOCSY);has been expanded and updated to reflect the new developments in NMR spectroscopy;has an additional 40 carefully selected basic problems;provides a set of problems dealing specifically with the quantitative analysis of mixtures using NMR spectroscopy;features proton NMR spectra obtained at 200, 400 and 600 MHz and 13C NMR spectra including routine 2D C–H correlation, HMBC spectra and DEPT spectra;contains a selection of problems in the style of the experimental section of a research paper;includes examples of fully worked solutions in the appendix;has a complete set of solutions available to instructors and teachers from the authors. Organic Structures from Spectra, Sixth Edition will prove invaluable for students of Chemistry, Pharmacy and Biochemistry taking a first course in Organic Chemistry.
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R 548.00
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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Beginning Java Data Structures and Algorithms
Though your application serves its purpose, it might not be a high performer. Learn techniques to accurately predict code efficiency, easily dismiss inefficient solutions, and improve the performance of your application.Key FeaturesExplains in detail different algorithms and data structures with sample problems and Java implementations where appropriateIncludes interesting tips and tricks that enable you to efficiently use algorithms and data structuresCovers over 20 topics using 15 practical activities and exercises Book DescriptionLearning about data structures and algorithms gives you a better insight on how to solve common programming problems. Most of the problems faced everyday by programmers have been solved, tried, and tested. By knowing how these solutions work, you can ensure that you choose the right tool when you face these problems.This book teaches you tools that you can use to build efficient applications. It starts with an introduction to algorithms and big O notation, later explains bubble, merge, quicksort, and other popular programming patterns. You’ll also learn about data structures such as binary trees, hash tables, and graphs. The book progresses to advanced concepts, such as algorithm design paradigms and graph theory. By the end of the book, you will know how to correctly implement common algorithms and data structures within your applications.What you will learnUnderstand some of the fundamental concepts behind key algorithmsExpress space and time complexities using Big O notation.Correctly implement classic sorting algorithms such as merge and quicksortCorrectly implement basic and complex data structuresLearn about different algorithm design paradigms, such as greedy, divide and conquer, and dynamic programmingApply powerful string matching techniques and optimize your application logicMaster graph representations and learn about different graph algorithmsWho this book is forIf you want to better understand common data structures and algorithms by following code examples in Java and improve your application efficiency, then this is the book for you. It helps to have basic knowledge of Java, mathematics and object-oriented programming techniques.
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Data Structures and Algorithms 1
The design and analysis of data structures and efficient algorithms has gained considerable importance in recent years. The concept of "algorithm" is central in computer science, and "efficiency" is central in the world of money. I have organized the material in three volumes and nine chapters. Vol. 1: Sorting and Searching (chapters I to III) Vol. 2: Graph Algorithms and NP-completeness (chapters IV to VI) Vol. 3: Multi-dimensional Searching and Computational G- metry (chapters VII and VIII) Volumes 2 and 3 have volume 1 as a common basis but are indepen dent from each other. Most of volumes 2 and 3 can be understood without knowing volume 1 in detail. A general kowledge of algorith mic principles as laid out in chapter 1 or in many other books on algorithms and data structures suffices for most parts of volumes 2 and 3. The specific prerequisites for volumes 2 and 3 are listed in the prefaces to these volumes. In all three volumes we present and analyse many important efficient algorithms for the fundamental computa tional problems in the area. Efficiency is measured by the running time on a realistic model of a computing machine which we present in chapter I. Most of the algorithms presented are very recent inven tions; after all computer science is a very young field. There are hardly any theorems in this book which are older than 20 years and at least fifty percent of the material is younger than 10 years.
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Computing in Cause-Effect Structures
This book focuses on numerous examples of tasks represented by c-e structure. Cause–effect (c-e) structures are dynamic objects devised for algebraic and graphic description of realistic tasks. They constitute a formal system providing means to specify or implement (depending on degree of description generality) the tasks. They can be transformed, thus come under simplification, in accordance with rules-axioms of their algebra. Also, their properties can be inferred from the axioms. One objective of this book is presentation, by many realistic examples, of computing capability of c-e structures, without entering into mathematical details of their algebra. In particular, how computing with natural numbers and in propositional calculus can be performed by c-e structures and how to specify behavior of data structures. But also demonstration of many other tasks taken from the area of parallel processing, specified as c-e structures. Another objective is modelling or simulation by means of c-e structures, of other descriptive systems, devised for tasks from various fields. Also without formalizing by usage of functions between the systems. This concerns formalisms such as reaction systems, rough sets, Petri nets and CSP-like languages. Also on such, where temporal interdependence between actions matters. The presentation of examples is prevalently graphic, in the form of peculiar nets, but accompanied by their algebraic and set-theoretic expressions. A fairly complete exposition of concepts and properties of the algebra of cause-effect structures is in the previous book appeared in the Lecture Notes in Networks and Systems series. But basic notions of c-e structures are here provided for understanding the examples.
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Probabilistic Data Structures for Blockchain-Based Internet of Things Applications
This book covers theory and practical knowledge of Probabilistic data structures (PDS) and Blockchain (BC) concepts. It introduces the applicability of PDS in BC to technology practitioners and explains each PDS through code snippets and illustrative examples. Further, it provides references for the applications of PDS to BC along with implementation codes in python language for various PDS so that the readers can gain confidence using hands on experience. Organized into five sections, the book covers IoT technology, fundamental concepts of BC, PDS and algorithms used to estimate membership query, cardinality, similarity and frequency, usage of PDS in BC based IoT and so forth.
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