Optimization with Constraints using Alternating Direction of Method of Multipliers I have an optimization problem of the form:
\begin{align*}
\text{minimize} &\quad f(x) + g(x) \\
\text{such that} & \quad Ax = b
\end{align*}
Where $f,g$ are both convex (but not differentiable), and I know the proximal operators for $f,g$ separately.
From reading Parikh and Boyd, this problem looks almost exactly like the kind solved by the Alternating Direction of Method of Multipliers (ADMM, aka Douglas-Rachford splitting).  Indeed, my problem almost exactly matches section 4.4 in the this text, except in the text, it is not constrained.   It is just:
\begin{align*}
\text{minimize} &\quad f(x) + g(x) \\
\end{align*}
In fact, in equation 4.9, they show that ADMM is equivalent to the idea of an augmented Lagrangian, which they derive by adding a constraint:
\begin{align*}
\text{minimize} &\quad f(x) + g(z) \\
\text{such that} & \quad x = z
\end{align*}
But I would like to add my own constraint, and I do not know how to do that.  
I believe this is possible, because there is a popular work called Robust PCA, where they modify the augmented Lagrangian to add their own constraint.  But their case seems quite different from mine, as they are optimizing over two variables (which nicely match up with the $x$ and $z$ in the augmented Lagrangian formulation), but I am not.  
How can I use ADMM to solve my problem?  If it is not possible, is there a more suitable method?

One approach using the Douglas-Rachford method (which can be viewed as being a special case of ADMM) is to reformulate your problem as minimizing $$
\underbrace{f(x) + g(y)}_{F(x,y)} + I_C(x,y),$$ where $C=\{(x,y)\mid Ax=b, x=y\}$.
You can now minimize $F(x,y) + I_C(x,y)$ using the Douglas-Rachford method. Because $F$ is a separable sum of $f$ and $g$, evaluating the prox-operator of $F$ reduces to separately evaluating the prox-operators of $f$ and $g$.  Evaluating the prox-operator of $I_C$ requires projecting onto $C$, which is just a linear algebra problem.
