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Course Criteria
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1.00 Credits
Approximation Algorithms deal with NP-hard combinatorial optimization problems by efficiently constructing a suboptimal solution with some specified quality guarantees. We study techniques such as linear programming and semidefinite programming relaxations, and apply them to problems such as facility location, scheduling, bin packing, maximum satifiability or vertex cover. Prerequisite: CSCI 1490 or 1570.
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1.00 Credits
Algorithms and data structures for fundamental geometric problems in two and three dimensions. Topics include point location, range searching, convex hull, intersection, Voronoi diagrams, and graph drawing. Applications to computer graphics, circuit layout, information visualization, and computer-aided design are also discussed. Prerequisite: CS 157 or written permission.
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1.00 Credits
A theory seminar focusing on algorithmic and combinatorial issues related to the design and analysis of communication networks for parallel and distributed systems. Topics include packet routing, circuit switching, distributed shared memory, fault tolerance, and more. Prerequisites: CS 155, 157, or equivalent.
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1.00 Credits
This advanced graduate course/seminar focuses on the mathematical foundations of algorithms for handling large amounts of data over networks. We'll read and discuss recent papers in information retrieval, search engines, link analysis, probabilistic modeling of the web and social networks, and more. Recommended: CSCI 1550 and CSCI 1570, or equivalent courses.
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1.00 Credits
Advanced topics in applications of probabilistic methods in design and analysis of algorithms, in particular to randomized algorithms and probabilistic analysis of algorithms. Topics include the Markov chains Monte Carlo method, martingales, entropy as a measure for information and randomness, and more. Prerequisites: CS 155. Recommended but not required: CS 157.
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1.00 Credits
The theoretical foundations of parallel algorithmics. Analysis of the most important models of parallel computation, such as directed-acyclic computation graphs, shared memory and networks, and standard data-exchange schemes (common address space and message-passing). Algorithmic techniques with numerous examples are cast mostly in the data-parallel framework. Finally, limitations to parallelizability (P-completeness) are analyzed. The content of the course is likely to change as technology evolves.
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1.00 Credits
Advanced topics in theoretical computer science are chosen from the following list: parallel computation, time and space complexity classes, circuit complexity, I/0 complexity, VLSI computation and nanocomputing.
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1.00 Credits
Nanoscale technologies employing materials whose smallest dimension is on the order of a few nanometers are expected to replace lithography in the design of chips. We give an introduction to computational nanotechnologies and explore problems presented by their stochastic nature. Nanotechnologies based on the use of DNA and semiconducting materials will be explored. Prerequisite: CSCI 0510.
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1.00 Credits
The theory of combinatorial optimization and how it is embodied in practical systems. Explores issues encountered in implementing such systems. Emphasizes the wide variety of techniques and methodologies available, including integer programming, local search, constraint programming, and approximation algorithms. Problems addressed may include: scheduling, coloring, traveling salesman tours, and resource allocation. Prerequisites: CSCI 0320 or CSCI 0360, and basic knowledge of linear algebra.
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1.00 Credits
Seminar-style course on advanced topics in cryptography. Example topics are zero-knowledge proofs, multi-party computation, extractors in cryptography, universal composability, anonymous credentials and ecash, interplay of cryptography and game theory. May be repeated for credit. Prerequisite: CSCI 1510 or permission of the instructor.
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