pap迭代算法的不收敛性

Question

我正在尝试实现下面文章的算法6（最终是算法7）https://arxiv.org/pdf/1610.03045.pdf

大部分看起来不错，但我不明白为什么我没有在$z$变量中实现收敛。我的代码似乎符合Alg 6的伪代码，所以我真的不确定这里发生了什么，但我相信这是变量[z{new}]或$z{hat}$的问题，因为它们应该获得越来越好的近似值，但是错误实际上在增加。你知道吗

def iterative_primal_dual_sketch_new(data, targets,
                                 left_sketch, right_sketch,
                             offset, tolerance,
                            max_iterations=10):
'''Perform the vanilla iterative_primal_dual_sketching.'''
X = data
y = targets
y = y[:,None] # change to (row, column) index not flat array
_lambda = offset 
n, d = X.shape


# initialise sketches nb. I have switched notation from the R in paper
feature_sketch = right_sketch
p = feature_sketch.shape[1]
# same as in paper for Pi but needs to be transposed for  right multiplication
sample_sketch = left_sketch
m = left_sketch.shape[1]

# sketch products and parameters for iteration
X_R = np.dot(X,feature_sketch) # just renaming to avoid unnecessary compute
double_sketch = (0.5*n)*np.dot(sample_sketch, X_R)
w_hat = np.zeros(shape=(d,1))
has_z_converged = False

for t in range(max_iterations):
    k = 0
    z = np.zeros(shape=(p,1))
    while not has_z_converged:
        z0 = z
        print("iteration on (t,k) ({},{})".format(t,k))
        print("start of loop: norm(z) = {}".format(norm(z0,2)))

        temp1 = (1/n)*(np.dot(X_R.T, y) -\
                       np.dot(X_R.T, np.dot(X,w_hat)) -\
                       np.dot(X_R.T, np.dot(X_R,z0)))
        temp2 = _lambda*np.dot(feature_sketch.T, w_hat) + _lambda*z0
        temp3 = temp1 - temp2 
        print("temp1 shape {}".format(temp1.shape))
        print("temp2 shape {}".format(temp2.shape))
        print("temp3 shape {}".format(temp3.shape))
        assert temp3.shape[0] == p

        # solve the linear system for z
        z_hat = np.linalg.solve(2*np.dot(double_sketch.T,double_sketch) \
                                +_lambda*np.eye(p),temp3)
        print("z_hat shape {}".format(z_hat.shape))
        print("Norm(z_hat) = {}".format(norm(z_hat)))

        z_new = z0 + z_hat 
        print("z_new shape {}".format(z_new.shape))
        print("norm(z_new) = {}".format(norm(z_new)))

        # convergence criterion: z_n+1 - z_n = z_hat
        if norm(z_hat,2) < tolerance:
            z = z_new
            print("Residual: {}".format(norm(z_hat,2)))
            print("Exiting loop.")
            has_z_converged = True
        else:
            z = z_new # flip the labelling so can just plug z in above code
            k += 1
            print("Looping again.")

    has_z_converged = False # reset so the loop is entered next time over    
    # Update dual
    alpha_dual = y - np.dot(X, w_hat) - np.dot(X_R, z)
    #print('Alpha shape {}'.format(alpha_dual.shape))

    # Update primal
    w_hat = (1/(n*_lambda))*np.dot(X.T, alpha_dual)
    print(w_hat.shape)
return w_hat, alpha_dual

左右草图仅定义为高斯矩阵，如： 1左边是大小为n/2 x n除以n的高斯矩阵。 2右边是大小d x d/2除以d的高斯矩阵

非常感谢您的帮助！你知道吗