14
We give a near-linear time 4-coloring algorithm for planar graphs, improving on the previous quadratic time algorithm by Robertson et al. from 1996. Such an algorithm cannot be achieved by the known proofs of the Four Color Theorem (4CT). Technically speaking, we show the following significant generalization of the 4CT: every planar triangulation contains linearly many pairwise non-touching reducible configurations or pairwise non-crossing obstructing cycles of length at most 5 (which all allow for making effective 4-coloring reductions).
The known proofs of the 4CT only show the existence of a single reducible configuration or obstructing cycle in the above statement. The existence is proved using the discharging method based on combinatorial curvature. It identifies reducible configurations in parts where the local neighborhood has positive combinatorial curvature. Our result significantly strengthens the known proofs of 4CT, showing that we can also find reductions in large ``flat" parts where the curvature is zero, and moreover, we can make reductions almost anywhere in a given planar graph. An interesting aspect of this is that such large flat parts are also found in large triangulations of any fixed surface.
From a computational perspective, the old proofs allowed us to apply induction on a problem that is smaller by some additive constant. The inductive step took linear time, resulting in a quadratic total time. With our linear number of reducible configurations or obstructing cycles, we can reduce the problem size by a constant factor. Our inductive step takes $O(n\log n)$ time, yielding a 4-coloring in $O(n\log n)$ total time.
In order to efficiently handle a linear number of reducible configurations, we need them to have certain robustness that could also be useful in other applications. All our reducible configurations are what is known as D-reducible.



RGBXY
You got a 5 dimensional system right there.
Its not that hard, except for the people that don’t understand multiple dimensions…
…which is most people, actually. So you’re kinda making the case against having a figure, because you would have to project your 5D object onto a 2D space, where both topology and graph theory simplify dramatically. Topological graph theory can tell us that there exist graphs with topologies that cannot be embedded into 2D or even 3D space without intersections, meaning you would have to make some sacrifices to draw these graphs within your framework.
But that’s not even how it works. If you allow for intersections, you can always draw a graph on a piece of paper. Which they do.
RGBXY
Red, Green, Blue, X, Y…
And guess what, T as well, Time, 6 dimensions.
Every gamer in the world is already processing in 6 dimensional visual memory space.
Nah, by your logic, they’re processing in much higher dimensions, one for each cone cell. But your brain processes these sensors into a two-dimensional spatial image that varies with time. (When a signal processing system performs this, we call it sensor fusion. And in fact, machine learning is a huge part of sensor fusion.) But even then, gamers aren’t just responding to the visual stimuli, but they’re tracking the abstractions of the game, such as players, enemies, terrain, etc. And then the physics engine inside a modern game typically implements either 2D or 3D space, plus time. And then the configuration space of all the objects a gamer needs to track adds dimensions.
But these high-dimensional objects…they really have structure that enables us to split them into groups of 1, 2, or 3. That’s not necessarily a helpful move for high-dimensional spaces in general.
Like I’m not saying that you literally never can or should visualize high-dimensional objects, e.g. in Hilbert spaces a lot of plane and 3D geometry intuition survives, but some situations are just not amenable to visual learning. (Conversely, of course, some situations require visual learning. But it’s important to be able to use all learning styles to some extent.)
A dimension is a measurement, by basic definition.
Therefore, all dimensions are dimensions.
Wait, did I just make an equality?
Edit: Fuckit, I just realized that Fahrenheit and Celsius are both the same dimension, just with a different scalar and constant offset. Not much different with Kelvin…
Nope. It’s a historical accident (and mistake IMO) that (mathematical) dimension and physical dimension have the same word “dimension”.
For example, in dynamical systems, we often work with so-called non-dimensionalized systems, i.e. we multiply the equations by the reciprocal of the physical dimension and end up with a system of unitless equations. This system may then be a N-dimensional system of unitless equations, i.e. you have N scalar equations.
Basically correct. Fahrenheit, Celsius, and Kelvin are all different units of the same dimensioned quantity, namely temperature.