People Innovation Excellence

ALGORITHM DESIGN AND ANALYSIS (4 Credits)

Learning Outcomes:

On successful completion of this course, student will be able to: LO1 – Explain fundamental concept of analysis algorithms; LO2 – Apply algorithm techniques and methods; LO3 – Solve a problem using specific algorithm; LO4 – Compare several algorithm design methods.

Topics:

  1. Analysis of Data Structures: Stack, Queue, Tree, and Binary Tree;
  2. Approximation algorithms: Subset-sum;
  3. Divide and Conquer: Recurrence;
  4. Mathematical induction and recursive function;
  5. Review I;
  6. Graph algorithms: Strongly connected components;
  7. Approximation algorithms: Vertex-cover;
  8. Analysis of Data Structures: Graph (Basic Search & Traversal), Priority Queue, and Heap;
  9. Divide and Conquer;
  10. Greedy methods: Huffman code;
  11. Analyzing Algorithms;
  12. Dynamic Programming: Rod Cutting;
  13. Review II;
  14. Characterizing Running Times;
  15. Amortized analysis;
  16. NP-Completeness Problems;
  17. Greedy methods;
  18. Approximation algorithms: Traveling salesperson;
  19. Graph algorithms: Shortest path;
  20. Randomized Algorithm: Hiring Problem;
  21. Dynamic Programming: Longest common subsequence;
  22. Dynamic Programming: Matrix Chain Multiplication;
  23. The Role of Algorithms in Programming;
  24. Dynamic Programming: Optimal binary search tree;
  25. NP-Completeness;
  26. Greedy methods: Activity-selection problem.

 


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