NDMI084 Introduction to approximation and randomized algorithms

Winter semester 2020/21

Petr Kolman

The weekly lecture takes place every Thursday from 9:00 - 10:30 pm, on Zoom.

The tutorials take place once in two weeks on Thursday from 2:00 - 3:30 pm and are led by Matej Lieskovský.

Lecture notes by J. Sgall (in Czech)

Syllabus

This course covers techniques for design and analysis of algorithms, demonstrated on concrete combinatorial problems. For many optimization problems it is impossible (or NP-hard) to design algorithms that finds an optimal solution fast. In such a case we study approximation algorithms that work faster, at the cost that we find a good solution, not necessarily an optimal one. Often we use randomness in design of (approximation and other) algorithms, which allows to solve problems more efficiently or even to solve problems that are otherwise intractable. Recommended for the 3rd year.

Covered topics / Tentative Schedule

• October 1 notes video
  • Introduction.
  • Randomized protocol for computing the average salary.
  • Optimization and NP-optimization problems.
  • Approximation ratio [WS 1.1, V1].
  • TSP - limits on approximation in the general setting.
• October 8 notes video-1 video-2
  • Metric TSP - 2-approximation, Christofides 1.5-approximation [WS 2.4, V 3.2].
  • Discrete probability - review of elementary definitions and properties (see Notes on probability by J. Matousek).
• October 15 notes video
  • Randomized Quicksort [MU 2.5, MR 1, KT 13.5].
  • Contention resolution in a distributed system - randomized protocol [KT 13.1].
  • Randomized global minimum cut algorithm [KT 13.2].
• October 22 notes video
  • Randomized global minimum cut algorithm [KT 13.2].
  • Scheduling on identical machines - local search [WS 2.3].
• October 29 notes video
  • Greedy algorithm for scheduling on identical machines (list scheduling, longest processing time first), online [WS 2.3].
  • Greedy algorithms for bin packing (first fit, best fit, any fit) [WS 3.3, V 9].
• November 5 notes video
  • Greedy algorithm for edge disjoint paths in graphs [KT 11.5].
  • Greedy algorithm for paths in graphs with capacities [KT 11.5].
• November 12 notes video
  • Randomized algorithms for satisfiability [WS 5.1-5.5, V 16].
    
    • Simple 1/2-approximation algorithm for MAX SAT (RAND SAT ALG), 7/8-approximation algorithm for MAX-E3-SAT.
    • Biased randomized approximation algorithm.
    • Approximation based on linear programming relaxation.
• November 19 notes video
  • Choosing the better of two solutions - 3/4 approximation for MAX SAT [WS 5.5].
  • Derandomization of RAND SAT ALG for MAX SAT by the method of conditional expectation [WS 5.3].
  • Algorithms for vertex and set cover [WS 1.2-1.6, 7.1, V 13-14].
    
    • The greedy algorithm and its analysis using dual linear program.
• November 26 notes video
  • Algorithms for vertex and set cover [WS 1.2-1.6, 7.1, V 13-14].
    
    • The primal algorithm, the dual algorithm, the primal-dual algorithm.
  • Parallel algorithm for maximum independent set problem [MR 12.3, also nice notes by Eric Vigoda at http://www.cc.gatech.edu/~vigoda/7530-Spring10/MIS.pdf].
• December 3 notes video
  • Parallel algorithm for maximum independent set problem, contd. [MR 12.3, the notes by E. Vigoda].
• December 10 notes video
  • Parallel algorithm for maximum independent set problem - Derandomization. [MR 12.3, the notes by E. Vigoda].
  • Universal hashing [MR 8.4, MU 13.3].
• December 17 notes video
  • 2-universal hashing and dynamic dictionary with expected constant time per operation.
  • Perfect hashing and static dictionary with constant time per lookup in the worst case.
  • Randomized testing of matrix multiplication in linear time [MR 7.1, MU 1.3].
• January 7, 2021 notes (extended on Jan 8, 2021) video
  • Randomized testing of polynomial identities [MR 7.1, MU 1.3].
  • Testing the existance of perfect matchings in bipartite and general graphs [MR 7.3, 12.4].
  • Parallel randomized algorithm for a perfect matching [MR 7.3, 12.4].

Textbooks

There is no required textbook but the material presented in the course is covered by several good books.

[WS] D. P. Williamson, D. B. Shmoys: The Design of Approximation Algorithms, Cambridge University Press, 2011.

[MR] R. Motwani, P. Raghavan: Randomized algorithms, Cambridge University Press, 1995.

[MU] M. Mitzenmacher, E. Upfal: Probability and Computing: Randomized Algorithms and Probabilistic Analysis, Cambridge University Press, 2005.

[V] V. V. Vazirani: Approximation Algorithms, Springer, 2001.

[KT] J. Kleinberg, E. Tardos: Algorithm Design, Pearson, 2006.

Other courses on approximation or randomized algorithms

• Approximation Algorithms by Chandra Chekuri
• Approximation Algorithms and Approximability by Mohammad R. Salavatipour
• Approximation Algorithms by Anupam Gupta and R. Ravi