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Class Description:

• Asymptotic Notation and Computational Models
• Divide-and-Conquer Algorithms
• Greedy Algorithms
• Dynamic Programming
• Graph Algorithms
• Network Flows and Linear Programming
• Computational Tractability and Complexity

For each of the topics above, we will consider a number of different application areas (e.g. divide-and-conquer algorithms for scientific computing, sorting, etc.). At the end of this course, students should be able to:

• Apply the appropriate paradigm of algorithm design to a given computational problem.
• Reason about the worst-case behavior and correctness of a given algorithm, and how these might compare to alternative approaches to the given problem.
• Be proficient in the application of basic algorithm approaches (e.g. divide-and-conquer, the greedy stragety, dynamic programming, etc.) as they apply to a number of different application areas (e.g. scientific computing, networks, scheduling, data analysis, etc.).

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