LE/EECS 4171 (3.00)

Advanced Topics in Algorithms

Description

This course will cover two or three topics selected from the following list. - Approximation algorithms for NP-hard problems, which may not find optimal solutions, but are guaranteed to be close to optimal. - Online algorithms, where the input arrives piece by piece and irrevocable decisions about the output must be made before the entire input is known. - Randomized algorithms, which make use of random choices and can sometimes be simpler than their deterministic counterparts while solving the problem with high probability or with good expected resource usage. - Parametrized algorithms, whose complexity is studied in finer detail to see how it depends on multiple parameters, rather than just input size, which can lead to good performance for certain classes of inputs. - Concurrent algorithms, where several processes cooperate to solve a problem and must deal with the difficulties of asynchrony, failures and partial knowledge of the system-wide state. - Parallel algorithms, where many processes operate synchronously on data in shared memory to solve a problem much more quickly than a single process can solve it. - Algorithmic game theory and mechanism design, where algorithms must be designed for multiple agents who have different goals. - Computational geometry, where the goal is to solve problems related to geometric objects such as points, lines, polygons and solid figures. - Combinatorial optimization algorithms for maximizing or minimizing some objective function over a finite set of objects Prerequisite: Cumulative GPA of 4.50 or better over all major EECS courses (without second digit "5"); LE/EECS 3101 3.00 ; SC/MATH 2030 3.00

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