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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.



Ok. And I’m still trying to convince people to stop taping magnets on the back of their phones, but they treat me like shit, as if I don’t know what a fucking compass sensor is…
I’m sorry that’s happening to you, but that’s completely irrelevant. The book I recommended predates AI, so it cannot be AI slop.
I never said a compass sensor was even an AI sensor.
Please learn to read before responding, magnets 100% fuck up compass sensors, AI or not.
Learn to read a fucking book, you claim to be smart, you should know how a fucking compass works.
I am literally an electrical engineering PhD student. I literally took a course on sensors last year. I know how compasses work. I did not mean to imply that compass sensors are AI sensors (whatever that means).
I’m acknowledging that your experience of people being mean to you for being right, while frustrating, is irrelevant to draw any conclusions about the book I recommended.