CR: COMP 3804 Learning Objectives: Difference between revisions
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* Greedy Algorithms: Minimum SPanning Trees, Huffman Encodings, ... | * Greedy Algorithms: Minimum SPanning Trees, Huffman Encodings, ... | ||
* Dynamic Programming: Edit Distance, Longest Common Sequence, ... | * Dynamic Programming: Edit Distance, Longest Common Sequence, ... | ||
* Linear Programming: What it is, how to express seveal problems as LP problems (Flow, Shortest Paths, Matching), ideas of simplex algorithm (or the randomized algorithm). | * Linear Programming: What it is, how to express seveal problems as LP problems (Flow, Shortest Paths, Matching), ideas of simplex algorithm (or the randomized algorithm). [This was never covered before] | ||
* NP-Completeness: search problems, polynomial time reductions, ideas of Cooks Theorem, and brief idea on how to cope with NP-Complete problems (1 lecture introduction to approximation algorithms, exhaustive search and search heuristics). | * NP-Completeness: search problems, polynomial time reductions, ideas of Cooks Theorem, and brief idea on how to cope with NP-Complete problems (1 lecture introduction to approximation algorithms, exhaustive search and search heuristics). | ||
Revision as of 16:20, 10 March 2011
Calendar Description
Assumed Background
COMP 2402 and COMP 2804.
Learning Objectives
Objectives for the whole course
- Designing algorithms, their correctness and efficiency.
- How to think about abstract computation - develop algorithmic intution - how to attack a problem algorithmically.
- Algorithmic techniques - learn key techniques (greed, divid-and-conquer, dynamic programming, LP, ..) - which technique to use for a given problem.
- How to compare one algorithmic solution versus another for a problem
- How to express the algorithmic solution - pseodcode - steps - analyze - argue that it is correct - essentially how will you explain your algorithmic solutions to others?
Topics
COMP 3804 offerings from Fall and Winter 2010-2011 by Anil and Michiel
- Introduction: Whats an Algorithm, Simple Divide and Conquer Algorithms (e.g. Multiplying two n-bit integers), merge-sort, bib-O notation, recurrence relation, masters theorem.
- Divide and Conquer: Some are already stated above, Median
- Graph Algorithms: Representation, DFS, BFS, Shortest Paths (including A* heuristic).
- Greedy Algorithms: Minimum SPanning Trees, Huffman Encodings, ...
- Dynamic Programming: Edit Distance, Longest Common Sequence, ...
- Linear Programming: What it is, how to express seveal problems as LP problems (Flow, Shortest Paths, Matching), ideas of simplex algorithm (or the randomized algorithm). [This was never covered before]
- NP-Completeness: search problems, polynomial time reductions, ideas of Cooks Theorem, and brief idea on how to cope with NP-Complete problems (1 lecture introduction to approximation algorithms, exhaustive search and search heuristics).