from cvxpy import *
from multiprocessing import Pool
import matplotlib.pyplot as plt
from numpy import linalg as LA
import numpy as np
import scipy as sp
import math

def solveX(data):
	a = data[0:3]
	u = data[3:6]
	z = data[6:9]
	rho = data[9]
	x = Variable(3,1)
	g = square(norm(x - a)) + rho/2*square(norm(x - z + u))
	objective = Minimize(g)
	p = Problem(objective, [])
	result = p.solve()
	return x.value

def main():
	#Solve the following consensus problem using ADMM:
	#Minimize sum(f_i(x)), where f_i(x) = square(norm(x - a_i))

	#Generate a_i's
	np.random.seed(0)
	a = np.random.randn(3, 10)

	#Initialize variables to zero
	x = np.zeros((3,10))
	u = np.zeros((3,10))
	z = np.zeros((3,1))

	rho = 5

	#Run 50 steps of ADMM
	iters = 0
	pool = Pool(processes = 10)
	while(iters < 50):

		#x-update: update each x_i in parallel
		temp = np.concatenate((a,u,np.tile(z, (1,10)),np.tile(rho, (10,1)).transpose()), axis=0)
		xnew = pool.map(solveX, temp.transpose())
		x = np.array(xnew).transpose()[0]

		#z-update
		znew = Variable(3,1)
		h = 0
		for i in range(10):
			h = h + rho/2*square(norm(x[:,i] - znew + u[:,i]))
		objective = Minimize(h)
		p = Problem(objective, [])
		result = p.solve()
		z = np.array(znew.value)

		#u-update
		for i in range(10):
			u[:,i] = u[:,i] + (x[:,i] - z.transpose())[0]

		iters = iters + 1

 	pool.close()
 	pool.join()
	
	print x

if __name__ == '__main__':
	main()

from cvxpy import *
from multiprocessing import Pool
import matplotlib.pyplot as plt
from numpy import linalg as LA
import numpy as np
import scipy as sp
import math

def solveX(data):
	a = data[0:3]
	u = data[3:6]
	z = data[6:9]
	rho = data[9]
	x = Variable(3,1)
	g = square(norm(x - a)) + rho/2*square(norm(x - z + u))
	objective = Minimize(g)
	p = Problem(objective, [])
	result = p.solve()
	return x.value

def main():
	#Solve the following consensus problem using ADMM:
	#Minimize sum(f_i(x)), where f_i(x) = square(norm(x - a_i))

	#Generate a_i's
	np.random.seed(0)
	a = np.random.randn(3, 10)

	#Initialize variables to zero
	x = np.zeros((3,10))
	u = np.zeros((3,10))
	z = np.zeros((3,1))

	rho = 5

	#Run 50 steps of ADMM
	iters = 0
	pool = Pool(processes = 10)
	while(iters < 50):

		#x-update: update each x_i in parallel
		temp = np.concatenate((a,u,np.tile(z, (1,10)),np.tile(rho, (10,1)).transpose()), axis=0)
		xnew = pool.map(solveX, temp.transpose())
		x = np.array(xnew).transpose()[0]

		#z-update
		znew = Variable(3,1)
		h = 0
		for i in range(10):
			h = h + rho/2*square(norm(x[:,i] - znew + u[:,i]))
		objective = Minimize(h)
		p = Problem(objective, [])
		result = p.solve()
		z = np.array(znew.value)

		#u-update
		for i in range(10):
			u[:,i] = u[:,i] + (x[:,i] - z.transpose())[0]

		iters = iters + 1

 	pool.close()
 	pool.join()
	
	print x

if __name__ == '__main__':
	main()

#Solve the following consensus problem using ADMM: #Minimize sum(f_i(x)), where f_i(x) = square(norm(x - a_i)) #Generate a_i's #Initialize variables to zero #Run 50 steps of ADMM #x-update: update each x_i in parallel #z-update #u-update