Algorithms in data structures

Tpreface his book is designed for a twosemester sequence in computer science, beginning with what is typically known as Data Structures and continuing with advanced data structures and algorithm analysis. It is appropriate for the courses from both the twocourse and threecourse sequences in “B.1 Introductory Tracks,” as outlined in the final report of the Computing Curricula 2001 project (CC2001)—a joint undertaking of the ACM and the IEEE. The content of the Data Structures course has been evolving for some time.
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Using the increasingly popular C language, this book teaches data structures from their theoretical conception through to their concrete realizations. It emphasizes structured design and programming techniques, and contains numerous debugged programming samples. For CS2 course in advanced programming or data structures in C.
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Strengthen your understanding of data structures and their algorithms for the foundation you need to successfully design, implement and maintain virtually any software system. Theoretical, yet practical, DATA STRUCUTRES AND ALGORITHMS IN C++, 4E by experienced author Adam Drosdek highlights the fundamental connection between data structures and their algorithms, giving equal weight to the practical implementation of data structures and the theoretical analysis of algorithms and their efficiency.
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CS2604: Data Structures and File Processing C++ Edition Cli ord A. Sha er Department of Computer Science Virginia Tech Copyright c 1995, 1996, 1998 COURSENOTES .You might believe that faster computers make it unnecessary to be concerned with e ciency. However... ⇒ • • • Data structures organize data A primary concern of this course is e ciency. The Need for Data Structures more e cient programs. More powerful computers ⇒ more complex applications. More complex applications demand more calculations. Complex computing tasks are unlike our everyday experience.
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The course focuses on strategies and techniques to efficiently store data (Data Structures) and to perform processing on such data in efficient ways (Algorithms), as well as on the analysis and design of such techniques. In this lecture, the following topics will be covered: Mathematical review; asymptotic and algorithm analysis; relationships and data structures; requential storage: Lists, queues, stacks, deques; hash tables; trees; priority queues and heaps; sort algorithms; graphs and graph algorithms; algorithm design techniques; complexity classes and NP completeness.
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In this topic, we will cover: Definition of a tree data structure and its components. Concepts of: Root, internal, and leaf nodes; parents, children, and siblings; paths, path length, height, and depth; ancestors and descendants; ordered and unordered trees; subtrees.
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A hierarchical ordering of a finite number of objects may be stored in a tree data structure. Operations on a hierarchically stored container include: Accessing the root, given an object in the container: Access the parent of the current object, find the degree of the current object, get a reference to a child, attach a new subtree to the current object, detach this tree from its parent.
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This topic will cover tree traversals: A means of visiting all the objects in a tree data structure, we will look at breadthfirst traversals and depthfirst traversals (Preorder and Postorder depthfirst traversals), applications, general guidelines.
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In computer science, a binary tree is a tree data structure in which each node has at most two children, which are referred to as the left child and the right child. In this talk, we will look at the binary tree data structure: Definition, properties, a few applications (Ropes, expression trees).
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A binary tree is a tree data structure in which each node has at most two children, which are referred to as the left child and the right child. This chapter introduce perfect binary trees: Definitions and examples, number of nodes, logarithmic height, number of leaf nodes, applications.
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In computer science, a balanced trees is a selfbalancing tree data structure that keeps data sorted and allows searches, sequential access, insertions, and deletions in logarithmic time. In this topic, we will: Introduce the idea of balance and a few examples.
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In computer science, binary search trees (BST), sometimes called ordered or sorted binary trees, are a particular type of containers: data structures that store "items" (such as numbers, names etc.) in memory. This topic covers binary search trees: Abstract Sorted Lists, background, definition and examples,...
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Typical algorithms and data structures textbooks are seven or eight hundred pages long, include chapters about software engineering and the programming language used in the book, and include appendices with yet more information about the programming language. Often they include lengthy case studies with tens of pages of specifications and code. Frequently they are hardcover books printed in two colors; sometimes they have sidebars with various sorts of supplementary material.
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This topic reviews the basic mathematics required in this course: A justification for a mathematical framework, the ceiling and floor functions, L’Hôpital’s rule, logarithms, arithmetic and other polynomial series, geometric series, recurrence relations, weighted averages, combinations.
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This chapter will provide a brief overview of C++. In this topic we will see: The similarities between C# and C++; some differences (including global variables and functions; the preprocessor, compilation, namespaces; printing), concluding with classes, templates, pointers memory allocation and deallocation.
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This topic will describe the storage of objects in containers, we will focus on linear orderings: Implicitly defined linear orderings (sorted lists), explicitly defined linear orderings. We will summarize this information and look briefly at: Hierarchical orderings, partial orderings, equivalence relations, adjacency relations.
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In this topic, we will look at: Justification for analysis, quadratic and polynomial growth, counting machine instructions, Landau symbols, BigQ as an equivalence relation, littleo as a weak ordering.
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In this topic, we will examine code to determine the run time of various operations. We will introduce machine instructions, we will calculate the run times of: operators +, , =, +=, ++, etc; control statements if, for, while, dowhile, switch; functions; recursive functions.
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In this topic, we will look at linked lists: The Node and List classes, accessors and mutators, the implementation of various member functions, stepping through a linked list, defining the copy and assignment operator, defining move constructors and move assignment operators, discussed efficiencies.
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In this presentation, we covered: Dealing with nodebased allocation with arrays; internally, it is still a linked list, only the nodes are contiguous in memory; it is no longer necessary to call the operating system for each new node; doubling the memory used is straightforward; to halve the memory used, we just follow the linked list.
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