Monte Carlo Simulation

Transcription

1 CPSC Handout Monte Carlo Simulation 1 Some History The term Monte Carlo Method is very general. A Monte Carlo method is a stochastic technique, meaning that it is based on using random numbers and probability to investigate problems. Monte Carlo methods are employed in a wide variety of fields including economics, finance, physics, chemistry, engineering, and even the study of traffic flows. Each field using Monte Carlo methods may apply them in different ways, but in essence they are using random numbers to examine some problem and approximate its outcome. As such Monte Carlo methods give us a way to model complex systems that are often extremely hard to investigate with other types of techniques. The term Monte Carlo was introduced by von Neumann and Ulam during World War II, as a code word for the secret work at Los Alamos; it was suggested by the gambling casinos at the city of Monte Carlo in Monaco. The Monte Carlo method was then applied to problems related to the atomic bomb. What we shall describe below as the basic Monte Carlo algorithm has been known since about A somewhat popular game at the time was to throw a needle onto a board ruled with parallel straight lines and then calculate the value of π from observations of the number of intersections between needle and lines. This algorithm was published by A. Hall in an article titled On an experimental determination of PI in the Journal Messenger of Mathematics in For a simulation of the experiment see The Monte Carlo method took off as a computational tool when computers became available and radical improvements over the basic algorithm were found, described below under Markov Chain Monte Carlo. 2 Basic Monte Carlo Simulation The basic idea of Monte Carlo is very simple. Since mean values of stochastic variables can be expressed as the integral of a variable times the probability density function, we could reverse the process by generating stochastic numbers and computing their mean by simple averaging. In theory, this should then give you an approximation to the integral. Let s make this more precise. Suppose we have an integral to evaluate of the form I = h(x)f(x)dx (1) where is some (usually high dimensional) domain with coordinates x and f(x) is a nonnegative function which satisfies f(x)dx = 1. (2) 1

2 Eq. 2 together with the non-negativity conditions allows us to interpret f as a probability density function on. With this interpretation in mind, we see that Eq. 1 is just the formula for the expectation value of h(x), where x are random variables on domain with pdf P (x) = f(x). If we now could generate N random variables x i (these will generally be vector coordinates) with pdf P (x) = f(x), we could approximate I in Eq. 1 by the experimentally observed average I =< h(x) > 1 N N h(x k ). (3) k=1 Even better, we could also estimate the standard deviation of the approximation to I and get some error estimates as well. The example we did earlier, computing the area of the unit circle, can be cast in this form. is the square x = [x, y] = [ 1 1] [ 1 1]. f(x, y) = 1/4 inside, zero elsewhere (just a uniform distribution), and h(x, y) = 1 if x 2 + y 2 < 1 (4) 0 otherwise. (5) In this case it is very easy to generate samples x k of pdf f(x) and the integral I should of course evaluate to π/4. As long as we work in one or two dimensions, the Monte Carlo method is inefficient, and better numerical approximations are available to compute the integral I. Such methods also sample the space by discrete samples x k, k = 1,... N, but usually not in a random way. For such methods, in an M dimensional space, the error in the approximation decreases with N according to N 1/M, (6) which is very very slow in high dimensions. The error in the Monte Carlo method on the other hand decreases as N 1/2, (7) independent of the dimension of! Many scientific problems can be cast into the form (1). Usually h(x) is a known and not too complicated function, but f(x) can only be computed with great effort, as it involves extensive simulation. The difficult part of Monte Carlo simulation lies in finding methods to actually generate random variables x that are distributed with pdf P (x) = f(x). One way around this would be to recast (1) into a different form by defining ĥ(x) = V h(x)f(x) ˆf(x) = 1/V, with V = dx. (8a) (8b) (8c) 2

3 In terms of these new hatted functions our integral (1) looks like I = ĥ(x) ˆf(x)dx, (9) and, at least for hypercubical domains it is easy to generate samples x k as ˆf is just the uniform distribution. Unfortunately this means that, if f was very small on large regions in, we are generating many samples x where it does not contribute much to the average (2). This is wasteful, because our new ĥ is expensive to evaluate since we have to compute f every time, which was expensive. For large problems this is so bad that the method is too slow to be useful in practice. 3 Markov Chain Monte Carlo: The Metropolis Algorithm In 1953 Metropolis and collaborators published a method to effectively sample P (x) = f(x). This algorithm is now known as the Metropolis algorithm. Nicolas Metropolis was a mathematician who worked on the construction of the Los Alamos MANIAC computer used for hydrogen bomb calculations. Edward Teller, the so-called father of the H bomb, is one of the co-authors on the original paper (Journal of Chemical Physics, 1953) describing the algorithm. Please read sections I and II of the paper. The rest is optional. The Metropolis algorithm computes a sequence of vectors x i, i = 1,..., N, that sample P (x). This sequence is highly correlated, but if I then randomly shuffle all the x i, for large enough N, they are random variables with pdf P (x). Some of the values x i can, and usually are, the same. The algorithm appears deceptively simple: 1. Pick any starting point for x Pick a candidate point y at random from some (small) neighborhood around x i. 3. efine p a = P (y)/p (x i ) 4. Generate a uniform random number r on [0 1]. 5. If r < p a set x i+1 = y, otherwise set x i+1 = x i, i.e., reject y. At this point you may ask: how do you pick a candidate point y at random from some (small) neighborhood around x i? This is where the art comes in. Some general conditions on this picking algorithm are known. If they are satisfied you can prove the algorithm converges for large N. The art lies in choosing the picking method such that it converges as fast as possible. Nobody knows how to do this best and techniques used are mainly based on experience in applications to specific problems. One method that works, but is almost as bad as the naive Monte Carlo method described in Section 2, is to just choose y totally at random. The problem is that in most applications P (x) is very small almost everywhere, so that p a (the probability to accept a candidate) is very small. 3

4 So if we find a point with a largish value of P (x) we should generate a cluster of points around there, but we immediately jump away from it and may never find this area in, which is vast, again. What is usually done is to choose y probabilistically, with a pdf (which has nothing directly to do with P (x)) that favors points close to the current point x. So the points x i tend to randomly walk around the domain, governed by some probabilities. Such a random walk is a Markov chain, hence the name Markov Chain Monte Carlo. Other Markov Chain Monte Carlo algorithms besides the Metropolis algorithm exist which have different strategies to sample P (x). If the Markov chain obeys certain rules (which we won t go into) it is provable that the Metropolis algorithm converges to a sampling of P (x). There are infinitely many ways to set up this Markov chain and how you do this precisely is critically important in getting the algorithm to converge in a reasonable amount of time. It is not uncommon to run a Monte Carlo algorithm for months on a cluster of computers. The sample code provided in mcfun2.m illustrates the naive and Metropolis algorithms on a trivial problem, for which we know the answer. The integral to compute is I = 500, 000 π x 2 y 2 e 50(x2 +y 2) dxdy. Some elementary analysis shows that I = 1. We interpret 50 π e 50(x2 +y 2) as P (x) and h(x) = 10, 000x 2 y 2. Note that though the domain is the entire plane, P (x) is very small everywhere except around the origin. The example provided computes the integral with the naive or with the Metropolis algorithm. The Markov chain implemented consists of jumping to another x in a unit square around x. The function reports the numerical approximation to I as well as the α confidence interval. A modest improvement can be observed in the Metropolis method which is all we can hope for in two dimensions. In the figure you can see how the points are sampled with the naive method and with the Metropolis algorithm (a) Naive MC (b) Metropolis (c) A zoom into the central region Figure 1: Computing an integral with the Monte Carlo method with sample code mcfun2.m. The domain is 8 < x, y < 8. The naive algorithm samples over the entire region, which is wasteful, whereas the Metropolis algorithm samples only where it matters. 4

5 4 Statistical Physics Monte Carlo simulation is widely used in statistical physics. Suppose we have a model with a large state space S. For example in molecular physics S would be the listing of the positions and velocities of all the molecules in the system. To simulate an N molecule system then results in a statespace of dimension 6N. The model of the system consists of a formula (or algorithm) to compute the energy E(s) of the system in state s. In molecular applications E could be computed by looking at all neigbouring molecules, evaluating their potential energy due to the interactions of their electron clouds, and summing over all possible pairs. At zero temperature the physical state of the system is then found by finding the state s 0 that minimizes E(s). At non-zero temperature the state fluctuates due to thermal agitation. Suppose we now measure some quantity F (s) which is expressed as some function of the state. For example, we might be interested in the average intermolecular distance or something like that. A basic law of statistical physics states that the measured value of F is the statistical average of F (s) over all the states s with weight factor P (s) (which can be interpreted as a pdf) given by P (s) = Z 1 e E(s)/kT, where k is the Bolzmann constant, T is the temperature in Kelvin, and the normalization constant Z is chosen to make P (s) a proper pdf with integral over state space equal to 1: Z = e E(s)/kT ds. So the observed value of F (s) is S < F >= Z 1 S F (s)e E(s)/kT ds, which is precisely in the form suitable for a Monte Carlo simulation. An example is the Ising model which is used to study phase transitions. So-called spins sit on the sites of a lattice in space. A spin s can take the value +1 or 1. These values could stand for the presence or absence of an atom, or the orientation of a magnetic atom (up or down). The energy of the model derives from the interaction between the spins. We take the energy per pair of neighbors s and s as Jss, where J > 0 is the spin-spin interaction. When the temperature T is high in relation to J, the spins are disordered: they take more or less random values. However, when the temperature T drops below a critical temperature, the spin system orders into a state of broken symmetry and most of the spins have the same sign. If we label the points (usually arranged on a grid) at which the spin variables s i sit i = 1,..., N, the magnetization M can be defined as M(s) = N s i. i=1 5

6 At zero temperature the energy is minimized when all the s i have the same sign and M will have some definite positive or negative value. At non-zero temperature the measured magnetization is given by < M >= Z 1 M(s)e E(s)/kT ds, S which can be computed with a Monte Carlo simulation. Below a critical temperature T c the system becomes magnetized. For a visualization see 6