- My talk on "introduction
to optimal transport paths". For readers not familar with the
subject, here is
the formal definition of transport paths as well as a family
of transport cost M_{alpha} defined on them.
- Answers from pure math.
- An optimal transport path between two probability measure
is a geodesic
in the metric space of probabilty measures ([7, theorem 5.1] and [2,
section 4]), where the metric on the space of probabilty measures is the intrinsic metric
induced by a nearmetric
defined on the space of transport plans ([2, section 4]). Here, a
nearmetric space is a generalized metric space in which the distance
satisfies a relaxed triangle inequality d(x,y)<=c(d(x,z)+d(y,z))
for some constant c no less than 1. See [2] for
details.
- An optimal transport path between two probability measure
is a solution to the Plateau's problem: Given a "boundary curve C" (
which is the difference of two probability measures here), find a
surface S (which is a rectifiable 1-current here) with boundary C of
the least "surface area" ( which is the M_{alpha} cost here).
So, an optimal transport path can be viewed as a special
kind of one dimensional "minimal surface".
- Answers from applied math.
- An optimal transport path is an efficient transport
system for transporting given sources to given targets.
Why do we study optimal
transport paths?
The study of optimal transport paths is motivated by many questions.
For instance:
- Motivations from applied math.
- Describe a general phenomena that it is more cost
efficient to transport items in a group than transport them
individually.
- Understand the language of the nature: why many living
and
nonliving systems prefer to adopt "branched" transport systems? What is
the role of "optimization" played in the formation of many fractal
typed systems in nature?
- Motivations from pure math.
- A geodesic between two points in the plane is a segment.
Is there a geodesic from two points to three points? Or,
given two
probabilty measures, what is a geodesics (if any) between
them?
- Soap films motivates the study of minimal
surfaces. What may the other "optimal" objects (e.g. trees,
leaves, etc) in nature lead to? Why do they prefer "branching
structures"?
- We know that a metric on a metric space may induce an
intrinsic
metric.
The triangle inequality is assumed to play an important role there.
When a distance
does not satisfy the triangle inequality (or only satisfies a relaxed
triangle inequality), will it still be able to
induce an intrinsic metric? If so, what does the intrinsic
metric
look like?
Numerical simmulation of
optimal transport paths
- Example: Given 100 random (red) points and a parameter
\alpha in [0,1], an optimal transport path from these points to the
origin looks like:
- Example 2: An optimal transport path from uniform
distributed points to the origin.
- Example 3: An optimal transport path from a unit
circle to its center with different parameter alpha
My future work in this field
- Transport flow
- Optimal fractals