CSC 427: Data Structures and Algorithm Analysis
- To appreciate the role of algorithms in problem solving and software design,
recognizing that a given
might be solved with a variety of algorithms. The student should be capable of
selecting among competing
and justifying their selection based on efficiency.
- To understand both the specifications and implementations of standard data
structures (lists, stacks,
linked structures, trees, maps), and be able to select and utilize appropriate data
structures in developing
- To develop programs using different problem-solving approaches
programming), and be able to recognize when one approach is a better fit for a given
- To be able to design and implement a program to model a real-world system,
and subsequently analyze its behavior.
- To recognize the importance of object-oriented techniques, and be able to
polymorphism to build upon existing code.
interacting classes, private data fields & public methods
static, final, private methods
generic classes and methods
examples: Comparable, Iterable, Collection, Set, List
implementing an interface, polymorphism
extending a class, overriding methods, polymorphism
calling super from a derived class
previous structures: array, ArrayList, LinkedList, Stack, Queue
low-level data structures
singly-linked vs. doubly linked, ListNode
non-linear structure, TreeNode, recursive processing
binary search tree, balanced variants (AVL tree, red-black tree)
heaps for priority queue, heap sort
hash function, collisions, load factor, rehashing
linear probing vs. chaining
Iterable interface: iterator
Iterator interface: next, hasNext, remove
List interface: get, set, add, remove, contains, indexOf, size, iterator, ...
ArrayList implementation: uses dynamic array
LinkedList implementation: uses doubly-linked list
Collections static methods on Lists: binary_search, sort, shuffle, max
Set interface: add, remove, contains, clear, size, iterator, ...
TreeSet implementation: uses red-black tree, ordered by compareTo
HashSet implementation: uses hash table with chaining
Map interface: get, put, remove, containsKey, keySet, size, ...
TreeMap implementation: uses red-black tree for key-value pairs
HashMap implementation: uses hash table with chaining
formal definition, rate-of-growth analysis, asymptotic behavior
searching & sorting
sequential search vs. binary search
insertion sort vs. selection sort vs. merge sort vs. heap sort
specialized sorts: frequency counts, radix sort
base case(s), recursive case(s), analysis via recurrence relation
brute force: contest problems, generate & test, ...
divide&conquer: binary search, merge sort, tree algorithms, ...
transform&conquer: presorting, balanced BST algorithms, heaps, ...
decrease&conquer: sequential search, selection sort, DFS, BFS, ...
greedy: job scheduling, Dijkstra's algorithm, Huffman codes, ...
backtracking: N-queens, blob count, Boggle, Sudoku, ...
dynamic: binomial coefficient, World Series puzzle, making change, ...