Since our loss is non-differentiable, we consider a subgradient method for optimization similar to . It requires to 1) Initialize ζ (0) and 2) Repeat ζ (t+1) = ζ (t) -α t g (t) , where g (t) ∈ ∂L(ζ (t) ), any subgradient of L at ζ (t) and α t is the learning rate parameter at t-th iteration.