Proximal operator

In mathematical optimization, the proximal operator is an operator associated with a proper, lower semi-continuous convex function $f$ from a Hilbert space ${\mathcal {X}}$ to $[-\infty ,+\infty ]$, and is defined by:

$\operatorname {prox} _{f}(v)=\arg \min _{x\in {\mathcal {X}}}\left(f(x)+{\frac {1}{2}}\|x-v\|_{\mathcal {X}}^{2}\right).$

For any function in this class, the minimizer of the right-hand side above is unique, hence making the proximal operator well-defined. The proximal operator is used in proximal gradient methods, which is frequently used in optimization algorithms associated with non-differentiable optimization problems such as total variation denoising.

Properties

The ${\text{prox}}$ of a proper, lower semi-continuous convex function $f$ enjoys several useful properties for optimization.

• Fixed points of ${\text{prox}}_{f}$ are minimizers of $f$: $\{x\in {\mathcal {X}}\ |\ {\text{prox}}_{f}x=x\}=\arg \min f$.
• Global convergence to a minimizer is defined as follows: If $\arg \min f\neq \varnothing$, then for any initial point $x_{0}\in {\mathcal {X}}$, the recursion $(\forall n\in \mathbb {N} )\quad x_{n+1}={\text{prox}}_{f}x_{n}$ yields convergence $x_{n}\to x\in \arg \min f$ as $n\to +\infty$. This convergence may be weak if ${\mathcal {X}}$ is infinite dimensional.
• The proximal operator can be seen as a generalization of the projection operator. Indeed, in the specific case where $f$ is the 0-$\infty$ characteristic function $\iota _{C}$ of a nonempty, closed, convex set $C$ we have that

${\begin{aligned}\operatorname {prox} _{\iota _{C}}(x)&=\operatorname {argmin} \limits _{y}{\begin{cases}{\frac {1}{2}}\left\|x-y\right\|_{2}^{2}&{\text{if }}y\in C\\+\infty &{\text{if }}y\notin C\end{cases}}\\&=\operatorname {argmin} \limits _{y\in C}{\frac {1}{2}}\left\|x-y\right\|_{2}^{2}\end{aligned}}$

showing that the proximity operator is indeed a generalisation of the projection operator.

• A function is firmly non-expansive if $(\forall (x,y)\in {\mathcal {X}}^{2})\quad \|{\text{prox}}_{f}x-{\text{prox}}_{f}y\|^{2}\leq \langle x-y\ ,{\text{prox}}_{f}x-{\text{prox}}_{f}y\rangle$.
• The proximal operator of a function is related to the gradient of the Moreau envelope $M_{\lambda f}$ of a function $\lambda f$ by the following identity: $\nabla M_{\lambda f}(x)={\frac {1}{\lambda }}(x-\mathrm {prox} _{\lambda f}(x))$.

• The proximity operator of $f$ is characterized by inclusion $p=\operatorname {prox} _{f}(x)\Leftrightarrow x-p\in \partial f(p)$, where $\partial f$ is the subdifferential of $f$, given by

$\partial f(x)=\{u\in \mathbb {R} ^{N}\mid \forall y\in \mathbb {R} ^{N},(y-x)^{\mathrm {T} }u+f(x)\leq f(y)\}$ In particular, If $f$ is differentiable then the above equation reduces to $p=\operatorname {prox} _{f}(x)\Leftrightarrow x-p=\nabla f(p)$.

See also

• Proximal gradient method

External links

• The Proximity Operator repository: a collection of proximity operators implemented in Matlab and Python.
• ProximalOperators.jl: a Julia package implementing proximal operators.
• ODL: a Python library for inverse problems that utilizes proximal operators.