update

baseline.py

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+import numpy as np
+import networkx as nx
+import copy
+import pandas as pd
+import xlwt
+import torch
+from torch import nn
+import torch.optim as optim
+from torch_geometric.utils import from_networkx
+from torch.utils.data import Dataset, DataLoader
+from torch_geometric.data import Data, Batch
+from torch_geometric.nn.conv import MessagePassing
+from torch_sparse import SparseTensor, matmul
+import torch.nn.functional as F
+
+
+def tgrad_qp(A, b, x):
+    # A: nodes * k * n
+    # X: nodes * n
+    # Y: nodes * k
+    '''grad_A = np.zeros(x.shape)
+    for i in range(x.shape[0]):
+        grad_A[i] = A[i].T @ (A[i] @ x[i] - b[i])
+    return grad_A'''
+    x_ = torch.unsqueeze(x, axis = -1)
+    b_ = torch.unsqueeze(b, axis = -1)
+
+    A_t = A.transpose(2,3)
+    grad_A = A_t @ (A @ x_ - b_)
+    # print(A.shape, x.shape, b.shape)
+    grad_A = torch.squeeze(grad_A, axis = -1)
+    return grad_A
+
+def torch_soft(x, tau):
+    return F.relu(x - tau) - F.relu( - x - tau)
+
+def opt_distance(x,opt):
+    error = 0
+    batch_size = x.shape[0]
+    num_of_nodes = x.shape[1]
+    error = np.linalg.norm(x-opt)**2
+    return error/num_of_nodes/batch_size
+
+def hist_nmse(x_hist,opt):
+    error = []
+    iteration = len(x_hist)
+    #print(iteration)
+    for k in range(iteration):
+        error.append(10*np.log10(opt_distance(x_hist[k].detach(),opt)))
+    return error
+
+
+#########################################################
+#                   PGEXTRA
+#########################################################
+
+
+def torch_PGEXTRA(W, A, b, max_iter, step_size,tau):
+    (batch_size, num_of_nodes, _, dim) = A.shape
+    init_x = torch.zeros((batch_size, num_of_nodes, dim))
+
+
+    (batch_size, num_of_nodes, dim) = init_x.shape
+    I = torch.unsqueeze(torch.eye(num_of_nodes), axis = 0)
+    I = I.repeat(batch_size, 1, 1)
+
+    W_hat = (W + I)/2
+
+    #initialization
+    k = 1
+    x_0 = init_x
+    x_12 = W @ x_0 - step_size * tgrad_qp(A, b, x_0)
+    x_1 = torch_soft(x_12, tau*step_size)
+
+    x_hist = [init_x,x_1] #add for plot
+    while (k < max_iter):
+
+        x_32 = W@x_1 + x_12 - W_hat@x_0 - \
+            step_size*(tgrad_qp(A, b, x_1)-tgrad_qp(A, b, x_0))
+        x_2 = torch_soft(x_32, tau*step_size)
+
+        x_0 = x_1
+        x_1 = x_2
+        x_12 = x_32
+
+        k = k + 1
+
+        x_hist.append(x_2)
+
+    return x_2,x_hist
+
+#########################################################
+#                   Prox-DGD
+#########################################################
+def torchProx_DGD(W, A, b, max_iter, step_size,tau):
+    (batch_size, num_of_nodes, _, dim) = A.shape
+    init_x = torch.zeros((batch_size, num_of_nodes, dim))
+
+
+    (batch_size, num_of_nodes, dim) = init_x.shape
+    I = torch.unsqueeze(torch.eye(num_of_nodes), axis = 0)
+    I = I.repeat(batch_size, 1, 1)
+
+    W_hat = (W + I)/2
+
+    #initialization
+    k = 1
+    x_0 = init_x
+    x_12 = W @ x_0 - step_size * tgrad_qp(A, b, x_0)
+    x_1 = torch_soft(x_12, tau*step_size)
+
+    x_hist = [init_x,x_1] #add for plot
+    while (k < max_iter):
+
+        x_32 = W@x_1 -  step_size*tgrad_qp(A, b, x_1)
+        x_2 = torch_soft(x_32, tau * step_size)
+
+        x_0 = x_1
+        x_1 = x_2
+        x_12 = x_32
+
+        k = k + 1
+
+        x_hist.append(x_2)
+
+    return x_2,x_hist
+
+