Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/218
Title: Randomized Algorithms (PDF)
Authors: R. Karger, Prof. David
Issue Date: 2005
Publisher: MIT
Description: This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, 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.
URI: http://hdl.handle.net/123456789/218
Appears in Collections:Mathematics & statistics
Mathematics & statistics

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Lecture (1).pdf97.06 kBAdobe PDFView/Open
Lecture (2).pdf87.75 kBAdobe PDFView/Open
Lecture (3).pdf86.74 kBAdobe PDFView/Open
Lecture (4).pdf99.96 kBAdobe PDFView/Open
Lecture (5).pdf132.53 kBAdobe PDFView/Open
Lecture (6).pdf141.77 kBAdobe PDFView/Open
Lecture (7).pdf121.87 kBAdobe PDFView/Open
Lecture (8).pdf75.34 kBAdobe PDFView/Open
Lecture (9).pdf145.38 kBAdobe PDFView/Open
Lecture (10).pdf120.66 kBAdobe PDFView/Open
Lecture (11).pdf122.44 kBAdobe PDFView/Open
Lecture (12).pdf137.53 kBAdobe PDFView/Open
Lecture (13).pdf119.37 kBAdobe PDFView/Open
Lecture (14).pdf42.75 kBAdobe PDFView/Open
Lecture (15).pdf104.15 kBAdobe PDFView/Open
Lecture (16).pdf90.2 kBAdobe PDFView/Open
Lecture (17).pdf108.81 kBAdobe PDFView/Open
Lecture (18).pdf83.6 kBAdobe PDFView/Open
Lecture (19).pdf120.43 kBAdobe PDFView/Open
Lecture (20).pdf106.62 kBAdobe PDFView/Open
Lecture (21).pdf98.62 kBAdobe PDFView/Open
Lecture (22).pdf119.56 kBAdobe PDFView/Open
Lecture (23).pdf105.92 kBAdobe PDFView/Open
Lecture (24).pdf86.83 kBAdobe PDFView/Open
Lecture (25).pdf122.23 kBAdobe PDFView/Open


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