PolytopeSampler is a Matlab implementation of constrained Riemannian Hamiltonian Monte Carlo for sampling from high dimensional disributions on polytopes. It is able to sample efficiently from sets and distributions with more than 100K dimensions.

PolytopeSampler samples from distributions of the form exp(-f(x)), for a convex function f, subject to constraints Aineq * x <= bineq, Aeq * x = beq and lb <= x <= ub.

The function f can be specified by arrays containing its first and second derivative or function handles. Only the first derivative is required. By default, f is empty, which represents a uniform distribution. If the first derivative is a function handle, then the function and its second derivatives must also be provided.

To sample N points from a polytope P, you can call sample(P, N). The function sample will

1. Find an initial feasible point
2. Run constrained Hamiltonian Monte Carlo
3. Test convergence of the sampling algorithm by computing Effective Sample Size (ESS) and terminate when ESS >= N. If the target distribution is uniform, a uniformity test will also be performed.

Extra parameters can be set up using opts. Some useful parameters include maxTime and maxStep. By default, they are set to

                        maxTime: 86400 (max sampling time in seconds)
                        maxStep: 300000 (maximum number of steps)

The output is a struct o, which stores samples generated in o.samples and a summary of the sample in o.summary. o.samples is an array of size dim x #steps.

We demonstrate PolytopeSampler using a simple example, sampling uniformly from a simplex. The polytope is defined by

>> P = struct;
>> d = 10;
>> P.Aeq = ones(1, d);
>> P.beq = 1;
>> P.lb = zeros(d, 1);

The polytope has dimension d = 10 with constraint sum_i x_i = 1 and x >= 0. This is a simplex. To generate 200 samples uniformly from the polytope P, we call the function sample().

>> o = sample(P, 200);
  Time spent |  Time reamin |                  Progress | Samples |  AccProb | StepSize |  MixTime
00d:00:00:01 | 00d:00:00:00 | ######################### | 211/200 | 0.989903 | 0.200000 |     11.2
Done!

We can access the samples generated using

>> o.samples

We can print a summary of the samples:

>> o.summary

ans =

  10×7 table

                     mean        std         25%         50%         75%      n_ess      r_hat 
                   ________    ________    ________    ________    _______    ______    _______

    samples[1]     0.093187    0.091207    0.026222    0.064326    0.13375    221.51    0.99954
    samples[2]     0.092815    0.086905    0.027018    0.066017    0.13221    234.59     1.0301
    samples[3]      0.10034    0.090834    0.030968    0.075631    0.13788    216.56     1.0159
    samples[4]      0.10531    0.092285    0.035363    0.077519     0.1481    235.25     1.0062
    samples[5]      0.10437    0.087634    0.034946    0.080095     0.1533    212.54    0.99841
    samples[6]       0.1029    0.093724    0.028774    0.074354    0.15135     227.6     1.0052
    samples[7]       0.1042    0.083084    0.038431    0.081964    0.15352    231.54     1.0008
    samples[8]     0.088778    0.086902    0.025565    0.062473    0.11837    229.69     1.0469
    samples[9]      0.10627     0.09074    0.036962    0.084294    0.15125    211.64    0.99856
    samples[10]     0.10184    0.084699    0.035981    0.074923    0.14578    230.63     1.0277

n_ess shows the effective sample size of the samples generated. r_hat tests the convergence of the sampling algorithm. A value of r_hat close to 1 indicates that the algorithm has converged properly.

See demo.m for more examples, including examples of sampling from non-uniform distributions.