[Stanford Algorithms: Design and Analysis, Part 2]

标签：pairs   via   statement   rap   square   short   www.   name   fast   

1. INTRODUCTION [Chapter 17]

   1. Overview, Resources, and Policies

       
   2. Pre-Course Survey

       
   3. All Lecture slides

       

2. TWO MOTIVATING APPLICATIONS [Chapter 18]

   1. Overview

       
   2. Application: Internet Routing (10 min)

       
   3. Application: Sequence Alignment (8 min)

       

3. INTRODUCTION TO GREEDY ALGORITHMS [Chapter 19]

   1. Introduction to Greedy Algorithms (12 min)

       
   2. Application: Optimal Caching (10 min)

       

4. A SCHEDULING APPLICATION  [Chapter 20]

   1. Problem Definition (5 min)

       
   2. A Greedy Algorithm (12 min)

       
   3. Correctness Proof - Part I (6 min)

       
   4. Correctness Proof - Part II (4 min)

       
   5. Handling Ties [Advanced - Optional] (7 min)

       

5. PRIM‘S MINIMUM SPANNING TREE ALGORITHM [Chapter 21]

   1. MST Problem Definition (11 min)

       
   2. Prim‘s MST Algorithm (7 min)

       
   3. Correctness Proof I (15 min)

       
   4. Correctness Proof II (8 min)

       
   5. Proof of Cut Property [Advanced - Optional] (11 min)

       
   6. Fast Implementation I (14 min)

       
   7. Fast Implementation II (9 min)

       

6. Homework 1

   1. Problem Set 1

      Problem Set This content is graded
   2. Optional Theory Problems

       
   3. Programming Assignment 1

      Programming Assignment This content is graded
      This is your last visited course section.Resume Course 

7. KRUSKAL‘S MINIMUM SPANNING TREE ALGORITHM [Chapter 22]

   1. Overview

       
   2. Kruskal‘s MST Algorithm (7 min)

       
   3. Correctness of Kruskal‘s Algorithm (9 min)

       
   4. Implementing Kruskal‘s Algorithm via Union-Find I (9 min)

       
   5. Implementing Kruskal‘s Algorithm via Union-Find II (13 min)

       
   6. MSTs: State-of-the-Art and Open Questions [Advanced - Optional] (9 min)

       

8. CLUSTERING [Chapter 23]

   1. Application to Clustering (11 min)

       
   2. Correctness of Clustering Algorithm (9 min)

       

9. ADVANCED UNION-FIND [Chapter 24]

   1. Lazy Unions [Advanced - Optional] (10 min)

       
   2. Union-by-Rank [Advanced - Optional] (12 min)

       
   3. Analysis of Union-by-Rank [Advanced - Optional] (14 min)

       
   4. Path Compression [Advanced - Optional] (14 min)

       
   5. Path Compression: The Hopcroft-Ullman Analysis I [Advanced - Optional] (9 min)

       
   6. Path Compression: The Hopcroft-Ullman Analysis II [Advanced - Optional] (11 min)

       
   7. The Ackermann Function [Advanced - Optional] (16 min)

       
   8. Path Compression: Tarjan‘s Analysis I [Advanced - Optional] (14 min)

       
   9. Path Compression: Tarjan‘s Analysis II [Advanced - Optional] (13 min)

       

10. HUFFMAN CODES [Chapter 25]

    1. Introduction and Motivation (9 min)

        
    2. Problem Definition (10 min)

        
    3. A Greedy Algorithm (16 min)

        
    4. A More Complex Example (4 min)

        
    5. Correctness Proof I (10 min)

        
    6. Correctness Proof II (12 min)

        

11. Homework 2

    1. Problem Set 2

       Problem Set This content is graded
    2. Optional Theory Problems

        
    3. Programming Assignment 2

       Programming Assignment This content is graded

12. INTRODUCTION TO DYNAMIC PROGRAMMING [Chapter 26]

    1. Overview

        
    2. Introduction: Weighted Independent Sets in Path Graphs (7 min)

        
    3. WIS in Path Graphs: Optimal Substructure (9 min)

        
    4. WIS in Path Graphs: A Linear-Time Algorithm (9 min)

        
    5. WIS in Path Graphs: A Reconstruction Algorithm (6 min)

        
    6. Principles of Dynamic Programming (7 min)

        

13. THE KNAPSACK PROBLEM [Chapter 27]

    1. The Knapsack Problem (9 min)

        
    2. A Dynamic Programming Algorithm (9 min)

        
    3. Example [Review - Optional] (12 min)

        

14. SEQUENCE ALIGNMENT [Chapter 28]

    1. Optimal Substructure (13 min)

        
    2. A Dynamic Programming Algorithm (12 min)

        

15. OPTIMAL BINARY SEARCH TREES [Chapter 29]

    1. Problem Definition (12 min)

        
    2. Optimal Substructure (9 min)

        
    3. Proof of Optimal Substructure (6 min)

        
    4. A Dynamic Programming Algorithm I (9 min)

        
    5. A Dynamic Programming Algorithm II (9 min)

        

16. Homework 3

    1. Problem Set 3

       Problem Set This content is graded
    2. Optional Theory Problems

        
    3. Programming Assignment 3

       Programming Assignment This content is graded

17. THE BELLMAN-FORD ALGORITHM: [Chapter 30]

    1. Overview

        
    2. Single-Source Shortest Paths, Revisited (10 min)

        
    3. Optimal Substructure (10 min)

        
    4. The Basic Algorithm I (8 min)

        
    5. The Basic Algorithm II (10 min)

        
    6. Detecting Negative Cycles (9 min)

        
    7. A Space Optimization (12 min)

        
    8. Internet Routing I [Optional] (11 min)

        
    9. Internet Routing II [Optional] (6 min)

        

18. ALL-PAIRS SHORTEST PATHS [Chapter 31]

    1. Problem Definition (7 min)

        
    2. Optimal Substructure (12 min)

        
    3. The Floyd-Warshall Algorithm (13 min)

        
    4. A Reweighting Technique (14 min)

        
    5. Johnson‘s Algorithm I (11 min)

        
    6. Johnson‘s Algorithm II (11 min)

        

19. Homework 4

    1. Problem Set 4

       Problem Set This content is graded
    2. Optional Theory Problems

        
    3. Programming Assignment 4

       Programming Assignment This content is graded

20. NP-COMPLETE PROBLEMS [Chapter 32]

    1. Overview

        
    2. Polynomial-Time Solvable Problems (14 min)

        
    3. Reductions and Completeness (13 min)

        
    4. Definition and Interpretation of NP-Completeness I (10 min)

        
    5. Definition and Interpretation of NP-Completeness II (7 min)

        
    6. The P vs. NP Question (9 min)

        
    7. Algorithmic Approaches to NP-Complete Problems (12 min)

        

21. FASTER EXACT ALGORITHMS FOR NP-COMPLETE PROBLEMS [Chapter 33]

    1. The Vertex Cover Problem (8 min)

        
    2. Smarter Search for Vertex Cover I (9 min)

        
    3. Smarter Search for Vertex Cover II (7 min)

        
    4. The Traveling Salesman Problem (14 min)

        
    5. A Dynamic Programming Algorithm for TSP (12 min)

        

22. Homework 5

    1. Problem Set 5

       Problem Set This content is graded
    2. Optional Theory Problems

        
    3. Programming Assignment 5

       Programming Assignment This content is graded

23. APPROXIMATION ALGORITHMS FOR NP-COMPLETE PROBLEMS [Chapter 34]

    1. Overview

        
    2. A Greedy Knapsack Heuristic (14 min)

        
    3. Analysis of a Greedy Knapsack Heuristic I (7 min)

        
    4. Analysis of a Greedy Knapsack Heuristic II (9 min)

        
    5. A Dynamic Programming Heuristic for Knapsack (11 min)

        
    6. Knapsack via Dynamic Programming, Revisited (10 min)

        
    7. Analysis of Dynamic Programming Heuristic (15 min)

        

24. LOCAL SEARCH ALGORITHMS [Chapter 35]

    1. The Maximum Cut Problem I (8 min)

        
    2. The Maximum Cut Problem II (9 min)

        
    3. Principles of Local Search I (8 min)

        
    4. Principles of Local Search II (10 min)

        
    5. The 2-SAT Problem (14 min)

        
    6. Random Walks on a Line (16 min)

        
    7. Analysis of Papadimitriou‘s Algorithm (14 min)

        

25. THE WIDER WORLD OF ALGORITHMS [Chapter 36]

    1. Stable Matching [Optional] (15 min)

        
    2. Matchings, Flows, and Braess‘s Paradox [Optional] (13 min)

        
    3. Linear Programming and Beyond [Optional] (11 min)

        
    4. Epilogue (1 min)

        

26. Homework 6

    1. Problem Set 6

       Problem Set This content is graded
    2. Optional Theory Problems

        
    3. Programming Assignment 6

       Programming Assignment This content is graded

27. Final Exam

    1. Final Exam

       Final Exam This content is graded

28. Finishing Up

    1. Post-Course Survey

        
    2. Generate Your Statement of Accomplishment

        

 
https://github.com/SSQ/Coursera-Stanford-Algorithms-Specialization

03/14/2019 Last few days:

https://www.youtube.com/playlist?list=PLXFMmlk03Dt5EMI2s2WQBsLsZl7A5HEK6

Minimum spanning tree:

https://www.cnblogs.com/infroad/p/9245794.html

[Stanford Algorithms: Design and Analysis, Part 2]

标签：pairs   via   statement   rap   square   short   www.   name   fast   

原文地址：https://www.cnblogs.com/ecoflex/p/10534706.html