Algorithm 9.
Proximal linearized 2-block ADMM with backtracking when the loss is differentiable and gradient is Lipschitz continuous
Input: |
Initialize: U(0), V(0), Λ(0), t |
Precompute: Difference matrix D, |
while not converged do |
for j = 1 to p do |
t = 1 |
while |
t = βt |
end while |
end for |
end while |
Output: U(k). |