# Proximal map for a convex optimization problem

I have a convex optimization problem

\begin{align*} \max_{P\, =\, (p_{ij})} &\sum_{ij} p_{ij} \big(a_{ij}-\log(p_{ij})\big)\\ \text{st}\quad &p_{ij}\in\mathcal{P}_{n, m} \; \text{and} \sum_{i=1}^n p_{ij} (b_i-1) = 0 \quad \forall j\\ \text{where,}\; &\mathcal{P}_{n, m} := \{(p_{ij}) \in \mathbb{R}_+^{n \times m}:\; \sum_{j} p_{ij} = 1,\forall i\} \end{align*}

This problem is related to A convex optimization problem. For that problem it was suggested to use proximal operator and it worked well but for this one the proximal map doesn't have a closed-form. How to go about this?

A bit of search shows that there are these generalized proximal mappings where one replaces squared Euclidean distance with other distance-like functions. The keywords here seem to be "entropic proximal mapping" and "mirror descent". Are there versions of splitting algorithms such as Douglas-Rachford for these generalized proximal operators?

• Am I correct that we interpret $p_{ij} \log(p_{ij})$ to be equal to $0$ if $p_{ij} = 0$? Dec 8, 2016 at 3:12
• @littleO indeed
– ie86
Dec 8, 2016 at 3:59
• By the way, how large are $m$ and $n$ typically? Dec 8, 2016 at 19:03
• @littleO usually m << n, n could be thousands, m is say ~10
– ie86
Dec 8, 2016 at 19:28

(This is my second attempt at a solution. My initial attempt can be found below.)

Your optimization problem can be expressed as $$\operatorname{minimize}_{p \in \mathbb R^{n \times m}} \quad F(p) + G(p)$$ where $$F(p) = \sum_{i,j} p_{ij}(\log(p_{ij}) - a_{ij}) + I_\Omega(p)$$ and $I_\Omega$ is the indicator function of the set $$\Omega = \{ p \in \mathbb R^{n \times m} \mid p_{ij} \geq 0 \, \forall \, i,j\}$$ (so $I_\Omega$ enforces nonnegativity on $p$) and $G$ is the indicator function of the set $$S = \{ p \in \mathbb R^{n \times m} \mid \sum_j p_{ij} = 1 \, \forall \, i \quad \text{and}\quad \sum_{i=1}^n p_{ij}(b_i - 1) = 0 \, \forall j \}.$$ Evaluating the prox-operator of $G$ requires projecting onto $S$, which is a linear algebra problem with a standard closed-form solution.

We do not have a closed-form expression for the prox-operator of $F$, but $F$ is still a very simple function (for example, it is fully separable). Can we not evaluate the prox-operator of $F$ numerically to high precision very efficiently?

Evaluating the prox-operator of $F$ reduces to evaluating the prox-operator of the function $w:\mathbb R \to \mathbb R \cup \{ \infty \}$ defined by $$w(x) = \begin{cases} x \log(x) & \quad \text{if } x \geq 0, \\ \infty & \quad \text{otherwise.} \end{cases}$$ (We interpret $x \log(x) = 0$ when $x = 0$.) To evaluate the prox-operator of $w$ at $\hat x$ (with parameter $t > 0$), we must solve \begin{align} \operatorname{minimize}_x & \quad x \log(x) + \frac{1}{2t} (x - \hat x)^2 \\ \text{subject to} & \quad x \geq 0. \end{align} If we visualize the graph of $v(x) = x \log(x) + \frac{1}{2t} (x - \hat x)^2$, we see that $v$ is initially decreasing (as we move away from the origin, to the right) but then eventually increases to $+\infty$. So, $v$ has a minimizer in the interval $(0,\infty)$ which can be found by setting $v'(x) = 0$. This yields: $$1 + \log(x) + \frac{1}{t}(x - \hat x) = 0 \iff x + t \log(x) = \hat x - t.$$ The function $x + t \log(x)$ is strictly increasing, and it ranges from $-\infty$ to $+\infty$ as $x$ ranges from $0$ to $+\infty$. Thus, there is a single value of $x$ for which $x + t \log(x) = \hat x - t$. I think this value of $x$ can be found to high accuracy with Newton's method (and I think it won't take too many iterations). This allows us to evaluate the prox-operator of $F$ efficiently.

Alternatively (and even better), we can express the solution to $$\frac{x}{t} + \log(x) = c$$ in terms of the Lambert W-function. Note that \begin{align} \frac{x}{t} + \log(x) = c & \iff \log(e^{x/t}) + \log(x) = \log(e^c) \\ &\iff x e^{x/t} = e^c \\ &\iff (x/t) e^{x/t} = \frac{e^c}{t}\\ &\iff x/t = W(e^c/t) \\ &\iff x = t W(e^c/t). \end{align} In Matlab, the Lambert W-function can be evaluated using lambertw.

Now that we have seen how to evaluate the prox-operators of both $F$ and $G$ efficiently, we are able to minimize $F(p) + G(p)$ using the Douglas-Rachford method (which solves convex problems of exactly this form).

Here is my initial attempt to solve the problem, which had a certain flaw (or issue, at least) which is discussed at the end.

The Chambolle-Pock algorithm solves convex optimization problems in the canonical form (or "graph form") minimize $f(x) + g(Cx)$, where the convex functions $f$ and $g$ are "simple" (meaning they have easy prox-operators) and $C$ is a matrix or a linear transformation. The Chambolle-Pock algorithm was extended by Condat (in this paper) to solve convex optimization problems of the form $$\tag{\spadesuit} \operatorname{minimize}_x \quad h(x) + f(x) + g(Cx)$$ where $f,g$ and $C$ are as above and the convex function $h$ is differentiable with a Lipschitz continuous gradient. Your problem has the form $(\spadesuit)$ where $C$ is the identity mapping and \begin{align} h(p) = -\sum_{ij} \, p_{ij}(a_{ij} - \log(p_{ij})) \\ \end{align} and $f$ is the indicator function of $$\{ p \in \mathbb R^{n \times m}_+ \mid \sum_j p_{ij} = 1 \, \forall i \}$$ and $g$ is the indicator function of $$\{ p \in \mathbb R^{m \times n} \mid \sum_{i=1}^n p_{ij}(b_i - 1) = 0 \, \forall j \}.$$

NOTE: A problem (or at least an issue) with this approach is that $h$ does not have a Lipschitz continuous gradient and cannot be extended to a differentiable function $\mathbb R^{n \times m}$. That might mean this approach doesn't work.