The Moreau-Yosida Regularization: Difference between revisions
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:<math>g_k(x) := \inf\limits_{y \in X} \left[ g(y) + k d(x,y) \right].</math> | :<math>g_k(x) := \inf\limits_{y \in X} \left[ g(y) + k d(x,y) \right].</math> | ||
==Examples== | ==Examples== | ||
(to be | * If <math>k = 0</math>, then by definition <math>g_0</math> is constant and <math>g_0 \equiv \inf\limits_{y \in X} g(y)</math>. | ||
* If <math>g</math> is ''not'' proper, then <math>g_k = +\infty</math> for all <math>k \geq 0</math>. | |||
Take <math>(X,d) := (\mathbb{R},|\cdot|)</math>. If <math>g</math> is finite-valued and differentiable, we can explicitly write down <math>g_k</math>. Then for a fixed <math>x \in \mathbb{R}</math>, the map <math>g_{k,x} : y \mapsto y^2 + k|x - y|</math> is continuous everywhere and differentiable everywhere except for when <math>y = x</math>, where the derivative does not exist due to the absolute value. Thus we can apply standard optimization techniques from Calculus to solve for <math>g_k(x)</math>: find the critical points of <math>g_{k,x}</math> and take the infimum of <math>g_{k,x}</math> evaluated at the critical points. One of these values will always be the original function <math>g</math> evaluated at <math>x</math>, since this corresponds to the critical point <math>y = x</math> for <math>g_{k,x}</math>. | |||
* Let <math>g(x) := x^2</math>. Then | |||
:<math>g_k(x) = \min \left\{ x^2 , \frac{k^2}{2} + k \left| x \pm \frac{k}{2} \right| \right\}.</math> | |||
==Results== | ==Results== |
Revision as of 00:01, 9 February 2022
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Motivation
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Definitions
Let be a metric space. A function is said to be proper if it is not identically equal to , that is, if there exists such that .
For a given function and , its Moreau-Yosida regularization is given by
Examples
- If , then by definition is constant and .
- If is not proper, then for all .
Take . If is finite-valued and differentiable, we can explicitly write down . Then for a fixed , the map is continuous everywhere and differentiable everywhere except for when , where the derivative does not exist due to the absolute value. Thus we can apply standard optimization techniques from Calculus to solve for : find the critical points of and take the infimum of evaluated at the critical points. One of these values will always be the original function evaluated at , since this corresponds to the critical point for .
- Let . Then
Results
Proposition. [1]
- If is proper and bounded below, so is . Furthermore, is continuous for all .
- If, in addition, is lower semicontinuous, then for all .
- In this case, is continuous and bounded and for all .
References
Possible list of references, will fix accordingly
Bauschke-Combette Ch 12.[2]; Santambrogio (6)[3]; Ambrosio-Gigli-Savare (59-61)[4]
- ↑ Craig, Katy C. Lower Semicontinuity in the Narrow Topology. Math 260J. Univ. of Ca. at Santa Barbara. Winter 2022.
- ↑ Bauschke, Heinz H. and Patrick L. Combettes. Convex Analysis and Monotone Operator Theory in Hilbert Spaces, 2nd Ed. Ch. 12. Springer, 2017.
- ↑ Santambrogio, Filippo. Optimal Transport for Applied Mathematicians: Calculus of Variations, PDEs, and Modeling Ch. 1.1. Birkhäuser, 2015.
- ↑ Ambrosio, Luigi, Nicola Gigli, and Giuseppe Savaré. Gradient Flows in Metric Spaces and in the Space of Probability Measures. Ch. 3.1. Birkhäuser, 2005.