1. The Markov Chain Monte Carlo Method Isabelle Stanton May 8, 2008 Theory Lunch

2. Monte Carlo vs Las Vegas Las Vegas Algorithms are randomized and always give the correct results but gamble with computation time Quicksort Monte Carlo algorithms have fixed running time but may be wrong Simulated Annealing Estimating volume

3. Markov Chains a memoryless stochastic process, eg, flipping a coin 5 6 4 32 1 1/61/6 1/6 1/61/6

4. Other Examples of Markov Chains Shuffling cards Flipping a coin PageRank Model Particle systems – focus of MCMC work

5. General Idea Model the system using a Markov Chain Use a Monte Carlo Algorithm to perform some computation task

6. Applications Approximate Counting - # of solutions to 3-SAT or Knapsack Statistical Physics – when do phase transitions occur? Combinatorial optimization – simulated annealing type of algorithms We'll focus on counting

7. Monte Carlo Counting How do you estimate the volume of a complex solid? Render with environment maps efficiently? Estimate an integral numerically?

8. (Picnic) Knapsack Holds 20 weighs 4 weighs 10 weighs 4 weighs 2 weighs 5 What is a solution?How many solutions are there?

9. Counting Knapsack Solutions Item weights: a = (a 0,...a n ) Knapsack size: a real number b Estimate the number of {0,1} vectors, x, that satisfy a*x ≤ b Let N denote the number of solutions

10. Naїve Solution Randomly generate x Calculate a*x If a*x ≤ b return 2 n else return 0 This will return N in expectation:  0*(2 n -N) + N*2 n / 2 n

11. Is this fast? Counterexample: a = (1,... 1) and b = n/3 Any solution has less than n/3 1's There are (n choose n/3)*2 n/3 solutions

12. no Pr(sample x, ||x|| ≤ n/3) < (n choose n/3)*2 -2n/3 In expectation, need to generate 2 n/3 x's before we get a single solution! Any polynomial number of trials will grossly underestimate N

13. Knapsack with MCMC Let M knap be a markov chain withstate space Ω(b) = {x | a*x ≤ b} This will allow us to sample a solution

14. Various M knap 000 001010100 011101110 111 a=(0,.5,.5) b = 1.5 a=(0,1,1) b = 1.5 001010100 110101011 000 001010100 110101

15. M knap Transitions Transitions  With probability 1/2, x transitions to x  Otherwise, select an i u.a.r. from 0 to n-1 and flip the ith bit of x. If x' is a solution, transition there. 000 001010100 011101110 111 001010100 110101 000 001010100 110101 a=(0,1,1) b = 1.5 0.5 1/6

16. Connected? Is M knap connected? Yes. To get from x to x' go through 0.

17. Ergodicity What is the stationary distribution of Knapsack?  Sample each solution with prob 1/N A MC is ergodic if the probability distribution over the states converges to the stationary distribution of the system, regardless of the starting configuration Is M knap ergodic? Yes.

18. Algorithm Idea Start at 0 and simulate M knap for enough steps that the distribution over the states is close to uniform Why does uniformity matter? Does this fix the problem yet?

19. The trick Assume that a 0 ≤ a 1... ≤ a n (0,1,2,…,n-1,n) Let b 0 = 0 and b i = min{b, Σ i a j } |Ω(b i-1 )| ≤ |Ω(b i )| - why? |Ω(b i )| ≤ (n+1)|Ω(b i-1 )| - why? Change any element of Ω(b i ) to one of Ω(b i-1 ) by switching the rightmost 1 to a 0

20. How does that help? |Ω(b)| = |Ω(b n )| = |Ω(b n )|/|Ω(b n-1 )| x |Ω(b n-1 )|/|Ω(b n-2 )| x... x |Ω(b 1 )|/Ω|(b 0 )| x |Ω(b 0 )| We can estimate each of these ratios by doing a walk on Ω(b i ) and computing the fraction of samples in Ω(b i-1 )‏ Good estimate since |Ω(b i-1 )| ≤ |Ω(b i )| ≤ (n+1)|Ω(b i-1 )|

21. Analysis Ignoring bias, the expectation of each trial is |Ω(b i-1 )|/|Ω(b i )| We perform t = 17ε -2 n 2 steps Focus on Var(X)/E(X)^2 in analyzing efficiency for MCMC methods

22. Analysis If Z is the product of the trials, E[Z] = П |Ω(b i-1 )|/|Ω(b i )| *Magic Statistics Steps* Var(Z)/(E[Z]) 2 ≤ ε 2 /16 By Chebyshev's: Pr[(1-ε/2)|Ω(b)| ≤ Z ≤ (1+ε/2)|Ω(b)| ] ≥ 3/4

23. Analysis We used nt = 17ε -2 n 3 steps This is a FPRAS (Fully Polynomial Randomized Approximation Scheme)‏ Except... what assumption did I make?

24. Mixing Time Assumption: We are close to the uniform distribution in 17ε -2 n 2 steps This is known as the mixing time It is unknown if this distribution mixes in polynomial time

25. Mixing Time What does mix in polynomial time?  Dice – 1 transition  Shuffling cards – 7 shuffles  ferromagnetic Ising model at high temperature – O(nlog n)‏ What doesn't?  ferromagnetic Ising model at low temperature – starts to form magnets

26. Wes Weimer Memorial Conclusion Slide The markov chain monte carlo method models the problem as a Markov Chain and then uses random walks Mixing time is important P# problems are hard Wes likes trespassing