In [1]:
import pymc as pm, numpy as np, matplotlib.pyplot as plt, seaborn as sns
%matplotlib inline

PyMC for Linear Programming

Stack Overflow http://stats.stackexchange.com/questions/105557/stochastic-programming-e-g-lp-with-mcmc

While I understand that there are far superior methods for solving LP problems (e.g. interior point algorithms), can MCMC be used to solve a stochastic LP problem?

PyMC2 can be combined with the LP solver of your choice to solve stochastic LP problems like this one. Here is code to do it for this very simple case. I've left a note on how I would change this for a more complex LP.

In [2]:
c1 = pm.Normal('c1', mu=2, tau=.5**-2)
c2 = -3
b1 = pm.Normal('b1', mu=0, tau=3.**-2)
In [3]:
@pm.deterministic
def x(c1=c1, c2=c2, b1=b1):
    # use an LP solver here for a complex problem
    arg_min = np.empty(2)
    min_val = np.inf
    for x1,x2 in [[0,0], [0, b1], [-b1, 0]]: # there are only three possible extreme points,
        if -x1 + x2 <= b1 and x1 >= 0 and x2 >= 0: #  so check obj value at each valid one
            val = c1*x1 + c2*x2
            if val < min_val:
                min_val = val
                arg_min = [x1,x2]
    
    return np.array(arg_min, dtype=float)
In [4]:
m = pm.MCMC(dict(c1=c1, c2=c2, b1=b1, x=x))
m.sample(20000, 10000, 10)
 [-----------------100%-----------------] 20000 of 20000 complete in 0.6 sec
In [5]:
pm.Matplot.plot(m)
Plotting b1
Plotting c1
Plotting x_0
Plotting x_1

Look at the weird joint distribution for $(x_1, x_2)$:

In [6]:
sns.jointplot(m.x.trace()[:,0], m.x.trace()[:,1], stat_func=None)
Out[6]:
<seaborn.axisgrid.JointGrid at 0x1bc11908>