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- Author:
-
Karger, David
- Subject:
- Science and Technology
- Institution Name:
- M.I.T.
- Collection:
-
MIT OpenCourseWare
- Grade Level:
- Post-secondary
- 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.
- Languages:
- English
- Material Type:
- Full Course, Homework and Assignments, Lecture Notes, Syllabi
- Media Format:
- Text/HTML, Downloadable docs
- Conditions of Use:
-
Creative Commons Attribution-Noncommercial-Share Alike 3.0
No restrictions on your remixing, redistributing, or making derivative works.
Give credit to the author, as required.
Your remixing, redistributing, or making derivatives works comes with some
restrictions, including how it is shared.
Your redistributing comes with some restrictions. Do not remix or make
derivative works.
Copyrighted materials, available under Fair Use and the TEACH Act for US-based
educators, or other custom arrangements. Go to the resource provider to see
their individual restrictions.
Randomized Algorithms, Fall 2002
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