• ☆ Yσɠƚԋσʂ ☆@lemmy.mlOP
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    2 months ago

    Can you explain how these planar graphs in full color help make the algorithm more clear? Please do elaborate on what additional explanatory power these would add.

    • over_clox@lemmy.world
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      2 months ago

      If you study color, you don’t go at it colorblind.

      Why are you defending the lack of full 4 color graphs, along with the 96 pages of text and math?

      • ☆ Yσɠƚԋσʂ ☆@lemmy.mlOP
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        2 months ago

        As I’ve already explained, and you promptly ignored, the paper isn’t about studying color. It’s about a specific mathematical algorithm that solves the theorem in linear time. The 96 page paper is about the algorithm. What part of that are you still struggling to comprehend?

        • over_clox@lemmy.world
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          2 months ago

          I solve Rubik’s Cubes blindfolded behind my back. Yay for me right? No joke either.

          Still, there’s no reason that mathematical experts can’t come up with an intuitive visual representation of their works.

          I followed the works of Steven Wittens, aka Wacko…

          https://acko.net/

          He actually visualizes mathematics.

          • PM_ME_VINTAGE_30S [he/him]@lemmy.sdf.org
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            2 months ago

            Because sometimes there is no intuitive visualization, or the visualization may even be deceptive. E.g. … the Coloring Problem is not literally about colors. It’s not even about maps. It’s about the abstraction itself. It’s about the math.

            • over_clox@lemmy.world
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              2 months ago

              RGBXY

              You got a 5 dimensional system right there.

              Its not that hard, except for the people that don’t understand multiple dimensions…

              • PM_ME_VINTAGE_30S [he/him]@lemmy.sdf.org
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                2 months ago

                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.

                • over_clox@lemmy.world
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                  2 months ago

                  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.

                  • PM_ME_VINTAGE_30S [he/him]@lemmy.sdf.org
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                    2 months ago

                    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.)