Optimal Transport in One Dimension: Difference between revisions
Jump to navigation
Jump to search
Line 8: | Line 8: | ||
== Distance Cost Example == | == Distance Cost Example == | ||
Consider the cost function <math> c(x,y) = |x-y|</math> along with <math> \mu = \frac{1}{2} \lambda_{[0,2]} </math> and <math> \nu = \frac{1}{2}\lambda_{[1,3]}</math> (where <math> \lambda </math> is the one-dimensional Lebesgue measure). | Consider the cost function <math> c(x,y) = |x-y|</math> along with <math> \mu = \frac{1}{2} \lambda_{[0,2]} </math> and <math> \nu = \frac{1}{2}\lambda_{[1,3]}</math> wherein <math> \sup spt(\mu) < \inf spt(\nu)</math> (where <math> \lambda </math> is the one-dimensional Lebesgue measure). |
Revision as of 03:59, 12 February 2022
In this article, we explore the optimal transport problem on the real line along with some examples.
Linear Cost Example
For this example, consider the cost function along with a given linear map . Moreover, if let be any transport plan, then by direct computation we see that
which suggests that this result only depends on the marginals of (wherein and are compactly supported probability measures). In fact, in such cases, every transport plan/map is optimal.
Distance Cost Example
Consider the cost function along with and wherein (where is the one-dimensional Lebesgue measure).