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(Complete Item Description)
- Abstract:
Thorough treatment of linear programming and combinatorial optimization. Topics include network flow, matching theory, matroid optimization, and approximation algorithms for NP-hard problems. 18.310 helpful but not required.
- Subject:
- Mathematics and Statistics
- Grade Level:
- Post-secondary
- Collection:
-
MIT OpenCourseWare
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(Complete Item Description)
- Abstract:
Studies how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Models of randomized computation. Data structures: hash tables, and skip lists. Graph algorithms: minimum spanning trees, shortest paths, and minimum cuts. Geometric algorithms: convex hulls, linear programming in fixed or arbitrary dimension. Approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.
- Subject:
- Science and Technology
- Grade Level:
- Post-secondary
- Collection:
-
MIT OpenCourseWare
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(Complete Item Description)
- Abstract:
In this graduate-level course, we will be covering advanced topics in combinatorial optimization. We will start with non-bipartite matchings and cover many results extending the fundamental results of matchings, flows and matroids. The emphasis is on the derivation of purely combinatorial results, including min-max relations, and not so much on the corresponding algorithmic questions of how to find such objects. The intended audience consists of Ph.D. students interested in optimization, combinatorics, or combinatorial algorithms.
- Subject:
- Mathematics and Statistics
- Grade Level:
- Post-secondary
- Collection:
-
MIT OpenCourseWare
