Introduction To Algorithms Cormen PPT

Introduction To Algorithms Cormen PPT

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Lectures:
A tentative schedule of lecture topics is given below.
Number
TopicSourceText
1
Introduction, administration, time and space complexity--
2
Basics: asymptotic notationPPT3.1-3.2
3
Basics: recurrences (mergesort)PPT4.1
4
Basics: recurrences continued, master theoremPPT4.3, 6.1-6.2
5
Sorting: intro to heapsortPPT6, 7.1-7.3
6
Sorting: heapsort, priority queuesPPT7.4
7
Sorting: quicksortPPT5.1-5.3
8
Sorting: quicksort average case analysisPPT5.4 last section
9
Sorting: linear time sorting algorithmsPPT8.1-8.2
10
Sorting: linear time algorithms continued;
Order statistics: selection in expected linear time		PPT8.3-8.4
9.1-9.2
11
Order statistics: selection in worst-case linear timePPT9.3
12
Review for examPPT
13
Structures: binary search treesPPT12.1-12.3
14
Structures: red-black treesPPT13.1-13.2
15
Structures: red-black trees (insertion)PPT13.3-13.4
16
Structures: skip listsPPT--
17
Structures: skip lists, hash tables PPT11.1-11.2
18
Structures: hash tables (hash functions)PPT11.3-11.4
19
Structures: hash tables (universal hashing)PPT11.3-11.4
20
Augmenting structures: dynamic order statisticsPPT14.1-14.2
21
Augmenting structures: interval treesPPT14.3
22
Graph algorithms: the basicsPPT22.1-22.3
23
Graph algorithms: BFSPPT22.3
24
Graph algorithms: DFSPPT23.1
25
Minimum spanning treesPPT23.2
26
Shortest paths: Bellman-FordPPT24.1-24.3
27
Shortest paths: DAG, Dijkstra's algorithmPPT
28
Finish Dijkstra's. Kruskals algorithm; disjoint setsPPT21.1-21.3, 23.2
29
Disjoint sets; amortized analysisPPT17.1-17.2
30
Amortized analysis continuedPPT17.3-17.4
31
Dynamic programming PPT15.1, 15.3
32
Dynamic programming (longest common subsequence)PPT15.4
33
Dynamic programming (knapsack problem)PPT
34
Greedy algorithms PPT16.1-16.2
35
NP-CompletenessPPT34.1-34.2
36
NP-Completeness continuedPPT34.1-34.2
37
NP-Completeness: reductionsPPT34.3-4
38
NP-Completeness: reductionsPPT34.3-4
39
Review for finalPPT--