Optimal Transport in One Dimension: Difference between revisions

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== 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> wherein <math> \sup spt(\mu) < \inf spt(\nu)</math> (where <math> \lambda </math> is the one-dimensional Lebesgue measure). Then, for any <math> (x,y) \in spt(\mu) \times spt(\nu) </math> we see that <math> c(x,y) = y-x </math>, which then immediately puts us back in the linear cost position, so any transport map/plan is also optimal for such costs.
Consider the cost function <math> c(x,y) = |x-y|</math> along with probability measures (on <math> \mathbb{R} </math>) <math> \mu </math> and <math> \nu . Then, for any <math> (x,y) \in spt(\mu) \times spt(\nu) </math> we see that <math> c(x,y) = y-x </math>, which then immediately puts us back in the linear cost position, so any transport map/plan is also optimal for such costs.


==Book Shifting Example==
==Book Shifting Example==
Consider the cost function
Consider the cost function

Revision as of 04:21, 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 probability measures (on ) and we see that , which then immediately puts us back in the linear cost position, so any transport map/plan is also optimal for such costs.

Book Shifting Example

Consider the cost function