Data Structures, Algorithms, and Applications in Java by Sartaj Sahni

Lecture	Content	Reading	Slides
1	Course overview and insertion sort.	Chapters 1 through 3.	Powerpoint
2	Insertion sort and practical complexities.	Section 3.5.	Powerpoint
3	Run-time measurement.	Chapter 4.	Powerpoint
4	Linear lists.	Sections 5.1-5.2.	Powerpoint
5	Array representation and array resizing.	Section 5.3.	Powerpoint
6	Walk through of code forArrayLinearList.	Section 5.3.	Powerpoint
7	Iterators. Linked representation of a linear list.	Sections 5.3 and 6.1.	Powerpoint
8	Walk through of code forChain. Head nodes, circular lists, doubly linked lists.	Sections 6.2 and 6.3.	Powerpoint
9	Simulated pointers and available-space lists.	Sections 7.1 and 7.2.	Powerpoint
10	Row-major and column-major indexing, and special matrices.	Sections 8.1, 8.2, and 8.3.	Powerpoint
11	Sparse matrices.	Section 8.4.	Powerpoint
12	Stacks--application to parentheses matching, towers-of-hanoi, railroad car rearrangement, and switchbox routing; array stacks.	Sections 9.1, 9.2, 9.5.	Powerpoint
13	Array and linked stacks.	Section 9.3 and 9.4.	Powerpoint
14	Nonapplicability of queues for parantheses matching, towers-of-hanoi, railroad problem with LIFO tracks, and switchbox routing. Application of queues to railroad problem with FIFO tracks, wire routing, and component labeling. Array and linked queues.	Sections 10.1-10.4, 10.5.1-10.5.3.	Powerpoint
15	Exam.	-	-
16	Dictionaries, linear list representation, and hashing.	Sections 11.1, 11.2, 11.3, and 11.5.	Powerpoint
17	Hashing and hash table design.	Section 11.5.	Powerpoint
18	LZW compression.	Section 11.6.	Powerpoint
19	Trees, binary trees, and properties.	Sections 12.1-12.3.	Powerpoint
20	Binary tree representation and operations.	Sections 12.4 and 12.5.	Powerpoint
21	Binary tree traversal methods-- preorder, inorder, postorder, level order. Reconstruction from two orders	Sections 12.6-12.8.	Powerpoint
22	Online equivalence classes.	Section 12.9.2.	Powerpoint
23	Application of priority queues to heap sort and machine scheduling. Min and max heaps.	Sections 13.1-13.3, 13.6.1, and 13.6.2.	Powerpoint
24	Initialization of min and max heaps. Height- and weight-biased leftist trees.	Sections 13.4.4 and 13.5.	Powerpoint
25	Winner and loser trees and application to k-way merging, run generation, and first-fit bin packing.	Chapter 14.	Powerpoint
26	Binary search trees and indexed binary search trees.	Sections 15.1-15.5.	Powerpoint
27	Definition of AVL trees. Graph applications and properties.	Sections 16.1, 17.1-17.3.	Powerpoint
28	Graph operations and representation.	Sections 17.4-17.7.	Powerpoint
29	Breadth-first and depth-first search. Application to path finding, connected components, and spanning trees.	Sections 17.8 and 17.9.	Powerpoint
30	Greedy method and application to bin packing, loading, and knapsack problems.	Sections 18.1, 18.2, 18.3.1, and 18.3.2.	Powerpoint
31	Exam.	-	-
32	Single source all destinations shortest paths algorithm.	Section 18.3.5.	Powerpoint
33	Kruskal's and Prim's minimum-cost spanning tree algorithms.	Section 18.3.6.	Powerpoint
34	Divide and conquer, and application to defective chessboard and min-max problem. Iterative min-max implementation.	Sections 19.1 and 19.2.1.	Powerpoint
35	Merge sort, natural merge sort, and quick sort.	Sections 19.2.2 and 19.2.3.	Powerpoint
36	Selection and closest pair of points.	Sections 19.2.4 and 19.2.5.	Powerpoint
37	Dynamic programming, 0/1 knapsack problem, recursive and iterative solutions.	Sections 20.1 and 20.2.1.	Powerpoint
38	Matrix multiplication chains, dynamic programming recurrence, recursive solution.	Section 20.2.2.	Powerpoint
39	Iterative solution to matrix multiplication chains.	Section 20.2.2.	Powerpoint
40	All pairs shortest paths.	Section 20.2.3.	Powerpoint
41	Single source shortest paths with negative edge weights.	Section 20.2.4.	Powerpoint
42	Solution space trees and backtracking.	Section 21.1.	Powerpoint
43	Branch and bound.	Section 22.1.	Powerpoint