1. CSCE 206 1 Monte Carlo Methods When you can’t do the math, simulate the process with random numbers Numerical integration to get areas/volumes Particle physics Interactions (e.g., biological) in which the level of response is variable “Monte Carlo” as in throwing dice to determine outcomes

2. CSCE 206 2 Random Numbers We don’t really want random numbers, we want “pseudorandom” numbers, because we want reproducible results in experiments. We want numbers that behave the way that random numbers would, but that are produced in a deterministic way. What do we mean by “behave the way that random numbers would”?

3. CSCE 206 3 Random Numbers (2) What do we mean by “behave the way that random numbers would”? If the numbers run 0.0 to 1.0, then 1/n of them should fall into any interval of length 1/n Similarly for 2 dimensions, 3 dimensions, etc. Shouldn’t be able to predict the next from the previous, or the next from the two previous, or the next from the three previous, etc. But we don’t want to require that the numbers be exactly evenly balanced either… NIST has a set of standard statistical tests

4. CSCE 206 4 Random Numbers (3) The RNG should generate the same numbers regardless of the machine used Different sequences of random numbers should come from different “seed” values The RNG should use minimal CPU and memory

5. CSCE 206 5 Random Numbers (4) RNGs usually produce integers that are uniformly distributed in the range of 1 to some maximum value M We want a maximal period, i.e., M distinct integers before a repetition We convert to reals, uniformly distributed in the range 0.0 to 1.0, by dividing by M (with appropriate mode conversion) We convert to reals with some chosen probabilistic distribution by using the random numbers in the distribution function

6. CSCE 206 6 Congruential Random Number Generators Consider powers of 2 modulo 13, say Power N 1 2 7 24=11 2 4 8 22=9 3 8 9 18=5 4 16=3 10 10 5 6 11 20=7 6 12 12 14=1 2**12 = 1 modulo 13

7. CSCE 206 7 Congruential RNGs (2) (Fermat’s Little) Theorem: If p is a prime number, and a is any integer, then a**(p-1) = 1 modulo p So the 2**12 = 1 mod 13 is not a coincidence Observation: The sequence of values assumed as we power up is more or less random among the values from 1 through p-1 This would be called a multiplicative congruential random number generator

8. CSCE 206 8 Congruential RNGs (3) Choose a prime p Choose a multiplier a Choose an addend b Compute x n+1 = (a*x n + b) modulo p If done correctly, then we get all the numbers 1 through p-1 before we repeat

9. CSCE 206 9 Congruential RNGs (4) Subprogram Subprogram for your assignment For a long time it was highly useful that 2**31 – 1 was a prime The “usual” 2s complement arithmetic for 32-bit words worked out nicely, so the whole multiplicative congruential stuff required no extra effort

10. CSCE 206 10 Lagged Fibonacci Generators Fill an array of values X j for j = 1, …, N (Note this means N seeds, not just one) Compute X j = X j-m + X j-n, for m,n < N “Fibonacci” because the series looks sort of like a Fibonacci series F n+2 = F n+1 + F n “Lagged” from looking at the subscripts

11. CSCE 206 11 Parallel RNGs For a very large Monte Carlo computation, we might need lots of random numbers We don’t want to have to compute these sequentially (very slow) Lagged Fibonacci RNGs allow at least N at a time to be computed in parallel Parallel RNGs are still an active research topic.

12. CSCE 206 12 The End

13. CSCE 206 13 Difference and Differential Equations The derivative of a function is the rate of change of that function A differential equation is an equation involving a derivative If one is driving at constant velocity, then the velocity is the rate of change of position dx/dt = v Starting at position zero, and travelling at velocity v for t time units, one travels v*t distance

14. CSCE 206 14 Euler’s Method dy/dt = f(y,t) (the differential equation) y(0) = y 0 (the initial condition) Approximate dy/dt by Δy/Δt = (y i+1 – y i )/ Δt = f(y i,t i ) so y i+1 = y i + Δt * f(y i,t i ) This is called Euler’s method. If we can compute the function f(y,t), then we can step forward a solution of the differential equation from time 0 to time whatever. In essence, approximate the function by trapezoids.

15. CSCE 206 15 The End