ECE 606 - Algorithm Design and Analysis
Lectures will be held in E5 5106 on Mondays from 8:30am – 11:30am.
Course teaching assistant
Morgan Xuelun Wu
Office hours: Thursdays 4:30-5:30 in EIT 3145
This course provides an introduction to the analysis and design of algorithms. Algorithms are at the very foundations of computing. It is important to understand how to design them, and analyse them for correctness and efficiency. It is also very important to able to recognize whether a given problem is intractable so you don’t naively seek efficient solutions where none may exist. This course will introduce you to a broad set of different types of computational problems and the most well known algorithms for solving them. Along the way we will learn how to prove the correctness of algorithms, analyse their computational complexity and understand where it fits in the hierarchy of computational complexity classes. The intent is to provide students with training to recognize the complexity of problems, understand the inherent tradeoffs of different solutions and take an appropriate approach to solving them. Emphasis will be placed on rigorous mathematical analysis of complexity, efficiency and correctness.
Data Structures and Algorithms, Probability, or consent of instructor.
The final grade for the course will be based on a final exam plus a five in-class quizzes and two assignments:
- In-class quizzes (5): Each worth 2.5% (lowest quiz dropped) = total 10%
- Assignments (2): Each worth 20% = 40%
- Final Exam: 50%
Introduction and Background
- Motivation and overview, stable matching
- Growth of functions, asymptotic notation
- Arrays, Linked-lists, Heaps, Priority Queues, B-trees
Design and Analysis Techniques
- Greedy algorithms
- Dynamic programming
- Graph representations, searching on graphs
- Spanning trees, shortest paths
- Network flow problems
- Turing Machine model of computation
- Optimization vs. decision problems
- P, NP, coNP, PSPACE, EXPTIME
- Cook and Karp Reductions, Gap Preserving Reductions
- Approximation algorithms
Probabilistic Analysis and Randomized Algorithms
- Introduction to randomized algorithms
- Probability theory and randomized min-cut
- Expected run-time of quicksort
- Markov and Chebyshev Inequalities, randomized median computation
There is no required textbook, course notes will be provided after each lecture. But most of the course is based on the following books and will be useful to take a look at them. Assigned (but unmarked) homework questions will often come from some of these texts:
- J. Kleinberg and E. Tardos, Algorithm Design, 2005
- This text is on 3 hour reserve in the Davis Centre Library.
- T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to Algorithms, 3rd edition, 2009
- This text is on 3 hour reserve in the Davis Centre Library
- It is also available for free online through the University of Waterloo library website.
- S. Arora and B. Barak, Computational Complexity: A Modern Approach, 2009
- M. Mitzenmacher and E. Upfal, Probability and Computing: Randomized Algorithms and Probabilistic Analysis, 2005
Papers and electronic references will be made available on the course website which is on LEARN.
Recipe for success
Attend lectures. Do complementary work at home. Ask questions. Have fun.
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