# Data Structures, Algorithms, and Applications in Java

### by

# Sartaj Sahni

# Real Video Lectures

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Lecture Content Reading Video

1 Course overview and insertion sort. Chapters 1 through 3. Not Available
2 Insertion sort and practical complexities. Section 3.5. Not Available
3 Run-time measurement. Chapter 4. Not Available
4 Linear lists. Sections 5.1-5.2. Not Available
5 Array representation and array resizing. Section 5.3.

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6 Walk through of code for ArrayLinearList. Section 5.3.

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7 Iterators. Linked representation of a linear list. Sections 5.3 and 6.1.

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8 Walk through of code for Chain. Head nodes, circular lists, doubly linked lists. Sections 6.2 and 6.3. Not Available
9 Simulated pointers and available-space lists. Sections 7.1 and 7.2.

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10 Row-major and column-major indexing, and

special matrices. Sections 8.1, 8.2, and 8.3.

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11 Sparse matrices. Section 8.4.

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12 Stacks--application to parentheses matching, towers-of-hanoi,

railroad car rearrangement, and switchbox routing;

array stacks. Sections 9.1, 9.2, 9.5.

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13 Array and linked stacks. Section 9.3 and 9.4.

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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.

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15 Exam. - -
16 Dictionaries, linear list representation, and hashing. Sections 11.1, 11.2, 11.3, and 11.5.

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17 Hashing and hash table design. Section 11.5.

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18 LZW compression. Section 11.6.

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19 Trees, binary trees, and properties. Sections 12.1-12.3. Not Available
20 Binary tree representation and operations. Sections 12.4 and 12.5. Not Available
21 Binary tree traversal methods-- preorder, inorder, postorder,

level order. Reconstruction from two orders Sections 12.6-12.8.

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22 Online equivalence classes. Section 12.9.2.

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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.

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24 Initialization of min and max heaps.

Height- and weight-biased leftist trees. Sections 13.4.4 and 13.5.

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25 Winner and loser trees and application to k-way merging, run generation,

and first-fit bin packing. Chapter 14.

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26 Binary search trees and indexed binary search trees. Sections 15.1-15.5. Not Available
27 Definition of AVL trees. Graph applications and properties. Sections 16.1, 17.1-17.3.

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28 Graph operations and representation. Sections 17.4-17.7.

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29 Breadth-first and depth-first search.

Application to path finding, connected components, and

spanning trees. Sections 17.8 and 17.9.

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30 Greedy method and application to bin packing, loading,

and knapsack problems. Sections 18.1, 18.2, 18.3.1, and 18.3.2.

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31 Exam. - -
32 Single source all destinations shortest paths algorithm. Section 18.3.5.

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33 Kruskal's and Prim's minimum-cost spanning tree algorithms. Section 18.3.6.

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34 Divide and conquer, and application to

defective chessboard and min-max problem.

Iterative min-max implementation. Sections 19.1 and 19.2.1.

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35 Merge sort, natural merge sort, and quick sort. Sections 19.2.2 and 19.2.3.

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36 Selection and closest pair of points. Sections 19.2.4 and 19.2.5.

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37 Dynamic programming, 0/1 knapsack problem, recursive

and iterative

solutions. Sections 20.1 and 20.2.1.

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38 Matrix multiplication chains, dynamic programming recurrence,

recursive solution. Section 20.2.2.

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39 Iterative solution to matrix multiplication chains. Section 20.2.2.

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40 All pairs shortest paths. Section 20.2.3.

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41 Single source shortest paths with negative edge weights. Section 20.2.4.

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42 Solution space trees and backtracking. Section 21.1.

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43 Branch and bound. Section 22.1.

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special matrices.

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railroad car rearrangement, and switchbox routing;

array stacks.

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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.

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level order. Reconstruction from two orders

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scheduling. Min and max heaps.

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Height- and weight-biased leftist trees.

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and first-fit bin packing.

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Application to path finding, connected components, and

spanning trees.

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and knapsack problems.

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defective chessboard and min-max problem.

Iterative min-max implementation.

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and iterative

solutions.

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recursive solution.

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