高级算法 (Fall 2023) / Course materials: Difference between revisions

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= References and further readings =
= References and further readings =
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: Michael Mitzenmacher and Eli Upfal.  
: Michael Mitzenmacher and Eli Upfal.  
:'''''Probability and Computing: Randomized Algorithms and Probabilistic Analysis'''''.
:'''''Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis,''''' 2nd Edition
:Cambridge University Press, 2005.
:Cambridge University Press, 2017.
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:Bernhard Korte and Jens Vygen.  
:Bernhard Korte and Jens Vygen.  
:'''''Combinatorial Optimization: theory and algorithms''''', 3rd edition.  
:'''''Combinatorial Optimization: theory and algorithms''''', 6th edition.
:Springer, 2008.
:Springer, 2018.
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Latest revision as of 06:43, 14 September 2023

Course textbooks

Rajeev Motwani and Prabhakar Raghavan.
Randomized Algorithms.
Cambridge University Press, 1995.
Vijay Vazirani.
Approximation Algorithms.
Springer-Verlag, 2001.

References and further readings

Michael Mitzenmacher and Eli Upfal.
Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis, 2nd Edition
Cambridge University Press, 2017.
Noga Alon and Joel Spencer.
The Probabilistic Method, 4th edition.
Wiley, 2016.
David P. Williamson and David Shmoys.
The Design of Approximation Algorithms.
Cambridge University Press, 2011.
Bernhard Korte and Jens Vygen.
Combinatorial Optimization: theory and algorithms, 6th edition.
Springer, 2018.
Nisheeth K. Vishnoi.
Lx = b: laplacian solvers and their algorithmic applications.
Foundations and Trends® in Theoretical Computer Science, 2012.
Lap Chi Lau.
Eigenvalues and Polynomials.
https://cs.uwaterloo.ca/~lapchi/cs860/notes/eigenpoly.pdf
Sanjoy Dasgupta, Christos Papadimitriou and Umesh Vazirani.
Algorithms.
McGraw-Hill, 2006.
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