Course Description:
In this course design and analysis of algorithms is studied. Methodologies include: divide and conquer, dynamic programming, and greedy strategies. Their applications involve: sorting, ordering and searching, graph algorithms, geometric algorithms, mathematical (number theory, algebra and linear algebra) algorithms, and string matching algorithms.
We study algorithm analysis - worst case, average case, and amortized, with an emphasis on the close connection between the time complexity of an algorithm and the underlying data structures. We study NP-Completeness and methods of coping with intractability. Techniques
such as approximation and probabilistic algorithms are studied for handling the NP-Complete problems.
Text: Introduction to Algorithms, Cormen, Rivest, Leiserson.
We study algorithm analysis - worst case, average case, and amortized, with an emphasis on the close connection between the time complexity of an algorithm and the underlying data structures. We study NP-Completeness and methods of coping with intractability. Techniques
such as approximation and probabilistic algorithms are studied for handling the NP-Complete problems.
Text: Introduction to Algorithms, Cormen, Rivest, Leiserson.
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