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Class Description:

This course will survey the design and analysis of algorithms. We will study several basic algorithmic paradigms (e.g. divide-and-conquer, dynamic programming, the greedy approach and randomization), their application to core problems in graph theory and optimization, as well as analysis of time and space complexity. Our focus will be on understanding these paradigms and studying the algorithmic problems that can be solved using that particular paradigm with respect to particular application areas.

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 strategy, dynamic programming, etc.) as they apply to a number of different application areas (e.g. scientific computing, networks, scheduling, data analysis, etc.).

Instructor:

Carola Wenk
Stanley Thomas, 303F
E-mail: cwenk -at- tulane -dot- edu
Phone: 504-865-5805
Office hours: TF 3pm-5pm, and by appointment

Teaching Assistants:

Avik Bhattacharya
Office hours: T 1pm-3pm, and by appointment; ST 309

Erin Stafford
E-mail: estaffor -at- tulane -dot- edu
Office hours: M 4pm - 6pm, and by appointment; ST 305

Last modified by Carola Wenk, cwenk -at- tulane -dot- edu,

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