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Computing proximal operator of a constrained convex function #117
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@lostella, thanks for your comment! You are right, if both have easy-to-compute proximal operators, then we can do it using In this post, however, I am considering the situation when projecting onto the constraint set is not obvious, e.g., for the indicator function mentioned above is of that type (it shows up in low-rank factor analysis problem). In such a case, computing proximal operators probably will be best done by a convex optimization solver. |
@Shuvomoy is \Sigma symmetric positive definite? |
Yes, it is! Is there a way to formulate it in |
Mateusz (See link above my post) just brought this to my attention – super interesting, that looks like a prox on a manifold. For just X on the SPDs or for both X and D on a product manifold of I think. JuMP recently got an interface to Manopt, or one could try this in Manopt directly even, if this is still something of interest. |
May I request for the feature extension to compute the proximal operator of a constrained convex function? For example, a constrained convex function that shows up in low rank factor analysis is:
where
I_P
is the indicator function of the convex set:In my Julia package
NExOS.jl
I have usedProximalOperators.jl
heavily when the functions are easy to compute. For now, whenever I need to deal with a constrained convex function, I am constructing the function object myself and computing the proximal operator byJuMP
and a solver supported by it. If there was a subroutine inProximalOperators.jl
, which would construct the constrained function object and compute the proximal operator via calling other solvers, it would be great. Of course, I completely understand if this is outside of the scope of the package.The text was updated successfully, but these errors were encountered: