Optimal Transport in One Dimension: Difference between revisions

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(Created page with "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 <m...")
 
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                                                                       <math> \int L(x-y) d \gamma = \int L(x) d \gamma - \int L(y) d \gamma = \int L(x) d \mu - \int L(y) d \nu </math>
                                                                       <math> \int L(x-y) d \gamma = \int L(x) d \gamma - \int L(y) d \gamma = \int L(x) d \mu - \int L(y) d \nu </math>


which suggest that this result only depends on the marginals of <math> \gamma </math> (wherein <math> \mu </math> and <math>\nu </math> are compactly supported probability measures). In fact, in such cases, every transport plan/map is optimal.
which suggests that this result only depends on the marginals of <math> \gamma </math> (wherein <math> \mu </math> and <math>\nu </math> are compactly supported probability measures). In fact, in such cases, every transport plan/map is optimal.
 


== 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> (where <math> \lambda </math> is the one-dimensional Lebesgue measure).

Revision as of 03:57, 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 (where is the one-dimensional Lebesgue measure).