fixed perturbed node penalty (0566282a) · Commits · 钟慕尧 / tvgl

ParallelTVGL.py

+9 −1

Original line number	Diff line number	Diff line
		@@ -33,6 +33,12 @@ def mp_parallel_tvgl((thetas, z0s, z1s, z2s, u0s, u1s, u2s,
		thetas_pre = []
		final_thetas = {}
		dimension = np.shape(thetas[0])[0]
		c = np.zeros((dimension, 3*dimension))
		c[:, 0:dimension] = np.eye(dimension)
		c[:, dimension:2*dimension] = -np.eye(dimension)
		c[:, 2dimension:3dimension] = np.eye(dimension)
		ct = c.transpose()
		cc = np.linalg.inv(np.dot(ct, c) + 2np.eye(3dimension))
		while iteration < MAX_ITER:

		""" Send last Z2, U2 values to next process,
		@@ -80,7 +86,9 @@ def mp_parallel_tvgl((thetas, z0s, z1s, z2s, u0s, u1s, u2s,
		u1s[i-1],
		u2s[i],
		beta,
		rho)
		rho,
		ct,
		cc)
		else:
		for i in range(1, n):
		a = thetas[i] - thetas[i-1] + u2s[i] - u1s[i-1]

+1 −7

Original line number	Diff line number	Diff line
		@@ -39,7 +39,7 @@ def group_lasso(a, beta, rho):
		return e


		def perturbed_node(theta_pre, theta, u1, u2, beta, rho):
		def perturbed_node(theta_pre, theta, u1, u2, beta, rho, ct, cc):

		""" Initialize ADMM algorithm """
		dimension = np.shape(theta)[0]
		@@ -50,12 +50,6 @@ def perturbed_node(theta_pre, theta, u1, u2, beta, rho):
		w = np.ones((dimension, dimension))
		uu1 = np.zeros((dimension, dimension))
		uu2 = np.zeros((dimension, dimension))
		c = np.zeros((dimension, 3*dimension))
		c[:, 0:dimension] = np.eye(dimension)
		c[:, dimension:2*dimension] = -np.eye(dimension)
		c[:, 2dimension:3dimension] = np.eye(dimension)
		ct = c.transpose()
		cc = np.linalg.inv(np.dot(ct, c) + 2np.eye(3dimension))

		""" Run algorithm """
		iteration = 0