Suppose we have a convex optimization problem with $N$ terms in the objective

\begin{array}{ll} \mbox{minimize} & \sum_{i=1}^N f_i(x)\\ \end{array}For example, we might be fitting a model to data and $f_i$ is the loss function for the $i$th block of training data.

We can convert this problem into consensus form

\begin{array}{ll} \mbox{minimize} & \sum_{i=1}^N f_i(x_i)\\ \mbox{subject to} & x_i = z \end{array}We interpret the $x_i$ as local variables, since they are particular to a given $f_i$. The variable $z$, by contrast, is global. The constraints $x_i = z$ enforce consistency, or consensus.

We can solve a problem in consensus form using the Alternating Direction Method of Multipliers (ADMM). Each iteration of ADMM reduces to the following updates:

\begin{array}{lll} % xbar, u parameters in prox. % called proximal operator. x^{k+1}_i & := & \mathop{\rm argmin}_{x_i}\left(f_i(x_i) + (\rho/2)\left\|x_i - \overline{x}^k + u^k_i \right\|^2_2 \right) \\ % u running sum of errors. u^{k+1}_i & := & u^{k}_i + x^{k+1}_i - \overline{x}^{k+1} \end{array}where $\overline{x}^k = (1/N)\sum_{i=1}^N x^k_i$.

The following code carries out consensus ADMM, using CVXPY to solve the local subproblems.

We split the $x_i$ variables across $N$ different worker processes. The workers update the $x_i$ in parallel. A master process then gathers and averages the $x_i$ and broadcasts $\overline x$ back to the workers. The workers update $u_i$ locally.

In [ ]:

```
from cvxpy import *
import numpy as np
from multiprocessing import Process, Pipe
# Number of terms f_i.
N = ...
# A list of all the f_i.
f_list = ...
def run_worker(f, pipe):
xbar = Parameter(n, value=np.zeros(n))
u = Parameter(n, value=np.zeros(n))
f += (rho/2)*sum_squares(x - xbar + u)
prox = Problem(Minimize(f))
# ADMM loop.
while True:
prox.solve()
pipe.send(x.value)
xbar.value = pipe.recv()
u.value += x.value - xbar.value
# Setup the workers.
pipes = []
procs = []
for i in range(N):
local, remote = Pipe()
pipes += [local]
procs += [Process(target=run_process, args=(f_list[i], remote))]
procs[-1].start()
# ADMM loop.
for i in range(MAX_ITER):
# Gather and average xi
xbar = sum(pipe.recv() for pipe in pipes)/N
# Scatter xbar
for pipe in pipes:
pipe.send(xbar)
[p.terminate() for p in procs]
```