
T79.250 Combinatorial Models and Stochastic Algorithms (4 cr) P
Spring 2005
Stochastic methods such as MCMC sampling, simulated annealing
and genetic algorithms are currently at the forefront of approximate
techniques for dealing with computationally demanding problems.
This course presents these algorithms and their underlying theory,
with the goal of learning to apply the methods to novel problems
and achieving a broad understanding of their common foundations.
[Current]
[General]
[Lectures]
[Tutorials]
[Exams]
[Literature]
Previous years:
[Spring 2003]
 Grade sheet (in Finnish, pdf) available
here.
 Deadline for programming assignments extended until Mon 30 May.
The assignments should be returned preferably on paper in the lecturer's
mailbox in Room A357 of the Computer Science building, or else per
email as either a postscript or a pdf file. Remember to include also
the program code used in your experiments.
 The course exam (Wed 4 May 1215 T1) will be "open book",
i.e. you can bring with you a copy of the lecture notes, plus
the tutorial problem sets and their solutions.
 The last lecture of the course is on Fri 22 April.
On Fri 29 April there will still be a tutorial session 1012.
 Deadlines for programming assignment fixed; for details
see below.
 Some really cool
applets
illustrating the ProppWilson algorithm from Jim Propp's home page.
 Lectures:
Pekka Orponen
18 Jan  29 Apr, Tue 911 & Fri 1214 room TB353.
 Tutorials:
Pekka Orponen, Fri 1012, room TB353.
 Examination:
Wed 4 May 1215 T1.
 Grading: Tutorial problems, programming assignment
and examination.
 Registration by
TOPI.
 Prerequisites: First two years' mathematics courses
including introductory probability theory (e.g. Mat2.090),
and programming skills (e.g. T106.230).
Familiarity with stochastic processes (Mat2.111), discrete
mathematics (Mat1.128), algorithm design (T106.410) and
computational complexity theory (T79.240) an asset.
 Grading: Exam 30 points, tutorial problems 10 points,
programming assignment 20 points, total 60 points.
 Programming assignment: The goal of this task is
to try out some of the sampling or optimisation methods
covered in the course on a model or problem of your own
choosing. Please submit a short problem description
(1/21 page) to P.O. between 18 March and 1 April;
if you have difficulty in picking a problem, come discuss
alternatives after class or during office hours (Wed 1213).
The deadline for submitting your report of the work,
containing discussion of the problem and method chosen,
and your experimental results (about 35 pages plus the
computer code) is Wed 25 May. You are free to choose
whatever programming language and environment is most
suitable for the task.
 Course brochure (in Finnish):
ps/pdf
 Part I: Markov Chains and Stochastic Sampling
Finite Markov chains and random walks on graphs
Markov chain Monte Carlo (MCMC) sampling
Estimating the convergence rate of Markov chains
Exact sampling with coupled Markov chains
 Part II: Combinatorial Models
Elements of statistical mechanics
Models: Ising, spin glasses, neural nets, NK landscapes
Random graphs: uniform and nonuniform
 Part III: Stochastic Algorithms
Stochastic local search
Simulated annealing
Approximate counting
MCMC simulations
Genetic algorithms
Local search for satisfiability
Combinatorial phase transitions
Structure of fitness landscapes [optional]
Lecture schedule
 Week 1. Markov chains: structure; recurrent and
transient states; existence and uniqueness of stationary
distribution for regular chains. Notes:
ps,
pdf.
 Week 2. Markov chains: convergence;
transients; reversibility. Notes:
ps,
pdf.
 Week 3. Markov chains: MCMC sampling;
convergence rate, conductance. Notes:
ps,
pdf.
 Week 4. Markov chains: canonical paths;
conductance and second eigenvalue;
coupling. Notes:
ps,
pdf.
 Week 5. Markov chains: coupling analysis
of a Gibbs sampler for graph colourings;
the ProppWilson algorithm. Notes:
ps,
pdf.
 Week 6. Combinatorial models: elements
of statistical mechanics; the Ising model,
spin glasses, neural networks. Notes:
ps,
pdf.
 Week 7. Combinatorial models:
NK networks, ErdösRényi random graphs. Notes:
ps,
pdf.
 Week 8. Combinatorial models:
ErdösRényi random graphs (cont'd)
 Week 9. Combinatorial models:
nonuniform random graphs. Notes to appear.
Slides
from a talk on networks at the Finnish Science Days,
Jan. 2005 (in pdf). Algorithms: simulated annealing.
 Week 10. Algorithms: simulated annealing, approximate counting.
 Week 11. Algorithms: MCMC estimation, genetic algorithms.
 Week 12. Algorithms: Genetic algorithms, combinatorial
phase transitions.
 Week 13. Algorithms: local search for satisfiability.
Slides Statistical
mechanics of KSAT.
Lecture notes from 2003
A
typeset (ps.gz) version of the lecture notes from Spring 2003
has kindly been made available by Vesa Hölttä. Note that the
ordering of topics in these notes is different from above: roughly,
Parts I and II are interchanged.
Problem sets will be available here as the course progresses.
The problems are likely to be largely the same as in
Spring 2003;
however the ordering will be different.
 Problem Set 1
(ps,
pdf)
 Problem Set 2
(ps,
pdf)
 Problem Set 3
(ps,
pdf)
 Problem Set 4
(ps,
pdf)
 Problem Set 5
(ps,
pdf)
 Problem Set 6
(ps,
pdf)
 Problem Set 7
(ps,
pdf)
 Problem Set 8
(ps,
pdf)
 Problem Set 9
(ps,
pdf)
 Problem Set 10
(ps,
pdf)
 Problem Set 11
(ps,
pdf)
 Problem Set 12
(ps,
pdf)
 Problem Set 13
(ps,
pdf)
Problems:
 General
E. Aarts, J. Lenstra (Eds.),
Local Search in Combinatorial Optimization.
John Wiley & Sons, New York, NY, 1997.
Y. BarYam,
Dynamics of Complex Systems.
AddisonWesley, Reading MA, 1997.
M. Habib, C. McDiarmid, J. RamirezAlfonsin, B. Reed (Eds.),
Probabilistic Methods for Algorithmic Discrete Mathematics.
SpringerVerlag, Berlin, 1998.
D. L. Stein (Ed.), Lectures in the Sciences of Complexity.
AddisonWesley, Reading MA, 1989.
 Finite Markov chains
E. Behrends, Introduction to Markov Chains,
with Special Emphasis on Rapid Mixing.
Vieweg & Sohn, Braunschweig/Wiesbaden, 2000.
P. Brémaud, Markov Chains: Gibbs Fields, Monte Carlo Simulation,
and Queues. SpringerVerlag, New York NY, 1999.
P. G. Doyle, J. Laurie Snell,
Random Walks and Electric Networks.
Mathematical Association of America, Washington DC, 1984.
O. Häggström, Finite Markov Chains and Algorithmic Applications.
Cambridge University Press, 2002.
D. G. Luenberger, Introduction to Dynamic Systems: Theory,
Models, and Applications. J. Wiley & Sons, New York NY, 1979.
 Algorithmic aspects of MCMC, with emphasis on rapid mixing
M. Jerrum, Counting, Sampling and Integrating:
Algorithms and Complexity.
Birkhäuser, Boston MA, 2003.
M. Jerrum, A. Sinclair,
"The Markov chain Monte Carlo method:
An approach to approximate counting and integration."
Approximation Algorithms for NPHard Problems.
(D. Hochbaum, ed.), pp. 482520. PWS Publishing, Boston MA, 1997.
A. Sinclair, Algorithms for Random Generation & Counting:
A Markov Chain Approach. Birkhäuser, Boston MA, 1993.
 Statistical mechanics, Ising model, spin glasses
R. Kindermann, J. L. Snell,
Markov Random Fields and Their Applications.
American Mathematical Society, Providence RI, 1980.
M. Mezard, G. Parisi, M. A. Virasoro (Eds.),
Spin Glass Theory and Beyond.
World Scientific, Singapore, 1987.
L. E. Reichl, A Modern Course in Statistical Physics, 2nd Ed..
J. Wiley & Sons, New York NY, 1998.
C. F. Stevens, The Six Core Theories of Modern Physics.
The MIT Press, Cambridge MA, 1995.
 Graph theory, random graphs, networks
B. Bollobás, Modern Graph Theory.
SpringerVerlag, New York NY, 1998.
B. Bollobás, Random Graphs, 2nd Ed.
Cambridge University Press, 2001.
R. Diestel,
Graph Theory, 2nd Ed.
SpringerVerlag, New York NY, 2000.
S. Janson, T. Luczak, A. Rucinski, Random Graphs.
J. Wiley & Sons, New York NY, 2000.
M. Karonski, "Random graphs." Handbook of Combinatorics,
Vol. 1 (R. L. Graham, M. Grötschel, L. Lovász, eds.), pp. 351380.
Elsevier, Amsterdam, 1995.
M. E. J. Newman,
"The structure and function of complex networks."
SIAM Review 45 (2003), 167256.
D. J. Watts, Small Worlds: The Dynamics of Networks Between Order
and Randomness. Princeton University Press, Princeton NJ, 1999.
 Simulated annealing, genetic algorithms
E. Aarts, J. Korst,
Simulated Annealing and Boltzmann Machines: A Stochastic Approach
to Combinatorial Optimization and Neural Computing.
J. Wiley & Sons, Chichester, 1989.
L. Kallel, B. Naudts, A. Rogers (Eds.),
Theoretical Aspects of Evolutionary Computing.
SpringerVerlag, Berlin, 2001.
C. R. Reeves, J. E. Rowe, Genetic Algorithms 
Principles and Perspectives. Kluwer Academic, Boston MA, 2003.
P. Salamon, P. Sibani, R. Frost,
Facts, Conjectures, and Improvements for Simulated Annealing.
Soc. Industrial & Applied Mathematics, Philadelphia PA, 2002.
M. D. Vose, The Simple Genetic Algorithm.
The MIT Press, Cambridge MA, 1999.
 Combinatorial phase transitions, fitness landscapes and related topics
[TCS main]
[Contact Info]
[Personnel]
[Research]
[Publications]
[Software]
[Studies]
[News Archive]
[Links]
Latest update: 23 June 2005.
Pekka Orponen.
