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 CSE 326: Data Structures, Summer 2003
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Fundamental abstract data types and their implementations as data structures. Asymptotic analyses of algorithms involving these data structures.

Precondition Concepts:

  • abstraction
    • modularity, encapsulation, information hiding
    • interface vs. implementation; multiple implementations of interface
    • language constructs: modules, user-defined data types
  • simple abstract data types and their implementations
    • stacks
    • queues
  • recursion
  • simple data structures (representations):
    • linked lists
    • trees (including preorder, inorder, postorder traversals)
  • simple analysis of running time
    • basics of big-O notation
    • comparison of implementations
  • searching (sequential, binary)
  • basics of discrete mathematics
    • sets and lists
    • relations and functions (injective, surjective, bijective)
    • modular arithmetic
  • logarithmic, exponential, floor, and ceiling functions

Precondition Abilities:

  • design and implement medium-sized programs (up to about 1000 lines), consisting of several (4-12) modules
  • understand and extend medium-sized (500+ lines) programs
  • read, write, use, and document (own and others') software components
  • write clients, implementations separately, given spec of interface
  • write simple proofs, including proofs by induction
  • analyze the asymptotic running times of simple algorithms involving, e.g., nested loops

Precondition Skills:

  • programming skill in C++ or Java
  • familiarity with creating, manipulating, and editing files within the framework of a multiuser timesharing system.

Postcondition Concepts

  • dictionary abstract data type, and its implementations
    • unsorted and sorted lists
    • binary search trees (without balancing)
    • balanced search trees: AVL trees and/or 2-3 trees and/or splay trees
    • hashing (separate chaining, open addressing, universal classes)
  • priority queue abstract data type, and its implementations
    • partially ordered trees
    • heaps and heapsort
    • Dijkstra's algorithm for single source shortest paths
  • disjoint union-find
    • up-trees with weighted union
    • find with path compression
    • Kruskal's algorithm for minimum spanning tree
  • adjacency list and adjacency matrix representations of graphs
  • sorting
    • mergesort
    • quicksort
    • decision trees and the lower bound

Postcondition Abilities

  • specify an abstract data type for a given application
  • design, analyze, and compare implementations

Postcondition Skills

  • familiarity with UNIX

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