COURSE DESCRIPTION. CS 241 Course Title Data Structures and Algorithms I. Course Coordinators

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1 COURSE DESCRIPTION Dept., Number Semester hours CS 241 Course Title Data Structures and Algorithms I 4 Course Coordinators Benjamin, Courtney Catalog Description Fundamental nature of information and storage structures and their manipulation. Linear lists, strings, arrays, stacks, representation of trees and graphs, multi-linked structures, iterative and recursive programming techniques, storage systems, structures, and allocation. Introduction to sorting and searching techniques. New Description The implementation of abstract data types (e.g. stacks, fifo queues, priority queues) in various manners using arrays and linked objects with attention to the implementations advantages and drawbacks; same abstract data types implemented with the Java Collections Framework. Introduction to information storage and retrieval systems (linear search, binary search, binary search trees) with special focus on the implementation and properties of hashing through linear probing and chaining. Introduction to sorting algorithms. Recursive programming techniques including binary tree traversals. May 2006 Textbook Faculty may choose the latest edition of either of the following: Frank Carrano and Janet J. Prichard; Data Abstraction and Problem Solving with Java, Walls and Mirrors; Addison Wesley Mitchell Waite and Robert Lafore; Data Structures and Algorithms in Java; Waite Group Press (Sams Publishing) References (latest edition is preferred) Alfred V. Aho, John E. Hopcroft, and Jeffrey D. Ullman; Data Structures and Algorithms; Addison Wesley Alfred V. Aho, John E. Hopcroft, and Jeffrey D. Ullman; The Design and Analysis of Computer Algorithms; Addison Wesley

2 Sara Baase and Allen Van Gelder; Computer Algorithms: Introduction to Design and Analysis; Addison Wesley Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest; Introduction to Algorithms; MIT Press and McGraw Hill Michael R. Garey and David S. Johnson; Computers and Intractability: A Guide to the Theory of NP-Completeness; W. H. Freeman Michael T. Goodrich and Roberto Tamassia; Foundations, Analysis, and Internet Examples; Wiley Ellis Horowitz and Sartaj Sahni; Fundamentals of Computer Algorithms; Computer Science Press. Ellis Horowitz and Sartaj Sahni; Fundamentals of Data Structures; Computer Science Press Donald E. Knuth; The Art of Computer Programming - Volumes 1 and 3; Addison Wesley Sartaj Sahni; Data Structures, Algorithms, and Applications in C++; McGraw Hill Robert Sedgewick; Algorithms; Addison Wesley Mark Allen Weiss; Data Structures and Algorithm Analysis Using Java; Benjamin Cummings Mark Allen Weiss; Data Structures & Problem Solving Using Java. Addison Wesley Niklaus Wirth; Algorithms and Data Structures; Prentice Hall Course Goals Objective 1: Students will learn to design software dependent upon a stack, a fifo queue, and a priority queue. Use the above abstract data types in programming applications Implement specialized versions of these abstract data types using java.util.vectors, java.util.linkedlists, and java.util.arrayslists and the adapter pattern Design adapters with customized APIs 2

3 Objective 2: Students will learn to use recursion in program construction. Implement recursive preorder, inorder, and postorder traversals of binary trees Diagram the action of recursive methods with recursion trees Generate combinations Use recursion trees to diagram backtracking (e.g. as in knapsack packing, solving a maze, or the 8-queen's problem) Objective 3: Students will learn alternate implements of stacks, fifo queues, and priority queues Implement stacks and queues various ways through arrays and dynamically allocated, linked objects Implement stacks and queues using classes from the Java Collections Framework. Discuss the advantages and drawbacks of different implementations Objective 4: Students will learn about Big-Oh. Evaluate the time complexity of fragments of code (e.g. a sequence of for loops vesus nested for loops) Describe how the time requirements for processing grows for different time complexities (e.g. linear, logarithmic, n lg n, quadratic, etc.) Use Big-Oh notation to evaluate different searching algorithms (linear search, binary search on a sorted array, binary search tree, hashing) 3

4 Objective 5: Students will begin learning collaborative skills by working on a group programming project. discuss the complications and benefits of working in a group, discuss the importance of responsibility, good communication skills, and good listening skills in collaborative work discuss the nature of group-decision making, negotiation, and leadership explain and perform the four phases of software engineering; specification, design, coding and testing; in conjunction with designing a product to satisfy a client s needs. Objective 6: Students will learn the characteristics that differentiate information storage and retrieval systems (e.g. time complexity of insertion, time complexity of retrieval, suitability for dynamic as opposed to static tables, suitability for internally stored data versus externally stored data), and students will undertake a case study of hashing via both linear probing (i.e. open linear addressing) and chaining. explain that insertion, look-up, and deletion in an unsorted array is O(N) explain that look-up in a sorted list with the binary search is O(lgN) but that insertion and deletion is O(N) diagrammatically illustrate insertions into a binary search tree and deletions diagrammatically illustrate how an unbalanced tree can result prove or argue that the height, and hence the longest look-up path, of a balanced binary search with N nodes is lgn explain that look-up, insertion, and deletion in a binary search tree is O(lgN) if the tree remains height-balanced explain that hashing gives the fastest look-up performance if the table never gets too full, but even so this performance cannot be guaranteed explain the characteristics of a good hash function diagram insertion, look-up, and deletion using linear probing, including collisions diagram insertion, look-up, and deletion using chaining explain load factor in conjunction with linear probing, and how as load factor increases so does the expected number of comparisons in a look-up explain the implementation of java.util.hashmap and use in practical applications 4

5 Prerequisites by Topic Ability to extend classes. Ability to trace through recursive methods involving a stack of referencing environments and explain the processing in terms of iterated work on the way up followed by iterated work on the way down. Comfort with one and two dimensional arrays. Comfort with insertions and deletions from the ends of linked lists of objects. Major Topics Covered in the Course Using abstract data types to solve problems: stacks, FIFO queues, and priority queues and various implementations including objects from the Java Collections Framework (e.g.vector, ArrayList, LinkedList) Big-oh notation, comparison of algorithms, the time/space trade-off. Recursive methods with two or more recursive calls; backtracking exemplified by applications such as knapsack-packing, solving a maze, the eight queens, the knight's tour Building and traversing binary trees Several information retrieval systems contrasted with respect to characteristics lending to their advantages and drawbacks in the design of applications; hashing (linear probing and chaining) given special attention Some internal sorting algorithms, including one O(N lg N ) sort The major stages of software development analysis and specification, design, coding and testing Experiencing the qualities that make for favorable performance on a team at work on a task requiring coordinated efforts over several weeks. 5