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Why Discrete Math Is Important

artofproblemsolving.com

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Re: Why Discrete Math Is Important

#111

Earlier quoted context omitted.

The theories that best model every known experiment are continuous. General relativity models large scale phenomena to incredible precision, and is continuous. Predictions made by it around 100 years ago are still being tested and found true, such as gravitational waves. The Standard Model, which models everything else, is continuous. A specific part of it, QED, is the most accurate theory known, agreeing with experi…

Other leading theories are non-continuous. LGQ example is an active area of research. > Every physics theory that we use to explain the universe is continuous Are you suggesting thete are no discrete models? Any discrete model may be similar to leading continuous models, so the explanatory power of both isn't relevant unless it can only be produced by continuous models.

>Other leading theories are non-continuous.

Which theories discretize all variables? I'm well aware people make models all the time, but they usually fail to explain all we observe or are toy models, like 2D TQFT models, not designed to mirror reality, but to fiddle with to see if they extend.

LQG, for example, treats space as quantized at the Plank length, but this has already been disproven experimentally [1,2], forcing LQG to reassess. LQG still has continuous parameters, such as symmetry, and is based on the same continuous math as GR and TQFTs. It's simply trying to discretize (somewhat) spacetime, but it certainly has not yet succeeded at that.

Hogan's holometer experiment [3] also has shown that the scales LQG wanted to discretize spacetime at are incorrect.

So there is at least 2 different experiments invalidating a central piece of LQG. Of course LQG can simply retrench and claim scales are smaller than the Plank length.

There is no experimental evidence pointing to LGQ as reality. It has not even been shown to reproduce GR in the semi-classical limit, so it may end up simply being mathematical fantasy. LQG also has lots of other technical problems that may in the end throw it on the heap of failed theories, of which there have been many. It makes no prediction not already covered under GR.

So, yes, I realize there are people trying to discretize the universe, but so far none of these theories have reproduced what we observe in experiment, and the only theories that have reproduced all we observe in experiment are continuous.

>Any discrete model may be similar to leading continuous models, so the explanatory power of both isn't relevant unless it can only be produced by continuous models.

Which model using only discrete variables can make the same predictions of GR or SM? I cannot think of one, I cannot find one, so it may be that so far the only models that work are continuous.

Heck, even going simpler - what discrete model can reproduce only GR? LQG so far cannot, despite significant effort trying to make it do so. So that surely puts the burden of proof on showing discrete models as equivalent to continuous ones.

So I'm game for something to break current models, because that would be cool. However, nothing has, and no theories is really even close to replacing SM and/or GR.

[1] https://arxiv.org/abs/1102.2784

[2] http://hal.in2p3.fr/tel-01219651/document

[3] https://en.wikipedia.org/wiki/Holometer

Re: Why Discrete Math Is Important

#112
post #61

> Many students, especially bright and motivated students, find algebra, geometry, and even calculus dull and uninspiring That was me. I grew up believing I hated math. Struggled all the way through middle & high school to AP calc and just found it incredibly boring and tedious. Ended up opting out of doing engineering/science in undergrad because I just couldn't stand doing all the math. Long story short, years late…

> It's a difference between obtaining an answer for a specific instance of a situation, and shedding light on some fundamental/universal property of the world.

They're also completely different questions in the sense of what you mean when you say "this result is correct."

The ball velocity is really a simplified model that gives you a correspondence truth (you verify by running the experiment and taking a measurement.) The exidtence proof gives you a coherence truth, i.e. there are no unknown factors and ur statement is absolutely true.

Just giving terminology to your intuition: coherence truth vs correspondence truth.

Re: Why Discrete Math Is Important

#113

Earlier quoted context omitted.

>Manifolds are just graphs with many vertices. Okay, I'll bite. How? What is the definition of the tangent space? Dimension?

the tangent space is the set of edges vector fields (sections of the tangent bundle) correspond to real-valued functions defined on edges dimension is always 2 ;) if you want higher dimension you have to consider higher-dimensional cliques beyond edges (that are 2-cliques): triangles, tetrahedra, and so on. But very often this is not necessary, even when discretizing 3d stuff. For example, for Poisson equation, and t…

Is a triangle a circle or a disk?

Re: Why Discrete Math Is Important

#114

Earlier quoted context omitted.

>Manifolds are just graphs with many vertices. Okay, I'll bite. How? What is the definition of the tangent space? Dimension?

the tangent space is the set of edges vector fields (sections of the tangent bundle) correspond to real-valued functions defined on edges dimension is always 2 ;) if you want higher dimension you have to consider higher-dimensional cliques beyond edges (that are 2-cliques): triangles, tetrahedra, and so on. But very often this is not necessary, even when discretizing 3d stuff. For example, for Poisson equation, and t…

That seems a weird choice of tangent space, surely a circle and a disk should have different tangent spaces?

Re: Why Discrete Math Is Important

#115
post #28

Earlier quoted context omitted.

A common use case is prove correctness or running time of algorithms.

Prove running time as in complexity analysis is more analysis than algebra though, right?

Depends. There's not a whole lot of analysis you can do on sufficiently complex recursive algorithms.

Re: Why Discrete Math Is Important

#116

Earlier quoted context omitted.

So is this the solution? The probability that all numbers are less than x is equal to x^n. So then you take the derivative of that to get the probability that the maximum us is exactly x. n x^n-1. Then calculate the expected value as integral from 0 to 1 of x n x^n-1 = n/n+1.

Too lazy to think deeply about it, but it sounds right. You start with the cumulative distribution function and get the pdf from it, which looks like what you're doing. Really simple solution. Problem is simple enough that this could be a standard HW problem in a probability course. Yet so many people (including myself) did not see it. We kept doing multiple integrals (n integrals for n points) and tried using induct…

it is (a standard HW problem). for bonus points, there's a connection between order statistics of the uniform distribution (such as max) and the beta distribution, of which the solution above is an example.

Re: Why Discrete Math Is Important

#117
post #35

Highly agree. As a current high school student, I've gone out of my way to study discrete math. Though I found calculus interesting, it's not particularly applicable to any part of CS except for a few concepts. OTOH, DM is incredibly useful for practically everything, which is what led me to seek it out.

if your interests tend toward machine learning, you may run into calculus again. in particular, if you keep going past the "train a neural network" phase (for which very basic calculus is fine) to the optimization problems that are under the hood of most ML algorithms, you wind up in the land of functional analysis.

Re: Why Discrete Math Is Important

#118
post #68

Earlier quoted context omitted.

I am intrigued by your ideas and wish to subscribe to your newsletter. How does Stokes' theorem in a discrete setting just amount to associativity? (I wondered about the sort of discrete differential geometry found e.g. here https://www.cs.cmu.edu/~kmcrane/Projects/DDG/paper.pdf and here https://arxiv.org/pdf/math/0508341.pdf but in that setting the situation seems to be more "Stokes' theorem is true by definition".)

I am intrigued by your ideas and wish to subscribe to your newsletter. Lol, this is not the first time that I am mocked here :) How does Stokes' theorem in a discrete setting just amount to associativity? Manifolds are modeled by graphs, and calculus on manifolds becomes linear algebra using the matrices naturally associated to these graphs. Consider a graph with n vertices and m edges. The most important matrix is t…

I didn't actually intend my comment as mockery. I'm very much for finding correspondences between discrete and continuous things. I just didn't see how to set things up so that Stokes turned into associativity.

I think this is in fact the same thing as the "true by definition" found in the second of my links above -- but I hadn't thought hard enough about how that cashes out concretely to see that you can express it as the associativity of a three-way product. Nice.

Re: Why Discrete Math Is Important

#119
post #118

Earlier quoted context omitted.

I am intrigued by your ideas and wish to subscribe to your newsletter. Lol, this is not the first time that I am mocked here :) How does Stokes' theorem in a discrete setting just amount to associativity? Manifolds are modeled by graphs, and calculus on manifolds becomes linear algebra using the matrices naturally associated to these graphs. Consider a graph with n vertices and m edges. The most important matrix is t…

I didn't actually intend my comment as mockery. I'm very much for finding correspondences between discrete and continuous things. I just didn't see how to set things up so that Stokes turned into associativity. I think this is in fact the same thing as the "true by definition" found in the second of my links above -- but I hadn't thought hard enough about how that cashes out concretely to see that you can express it…

stokes theorem is often written (in a continuous setting) as

     = 
(the adjoint of the boundary operator is the exterior derivative). In the discrete case, these integrals are actually products of matrices. There is nothing too deep here.

Edit: regarding the "true by definition" issue, you can do one of two things. (1) Build the definition of the boundary and exterior derivative independently and then verify that Stokes theorem holds. Or (2) build the definition of only one of these two operators, and then define the other one as its adjoint. In that second case, Stokes theorem is true by definition. But it doesn't matter too much, these are just two different ways to write the same thing.

Re: Why Discrete Math Is Important

#120

Earlier quoted context omitted.

the tangent space is the set of edges vector fields (sections of the tangent bundle) correspond to real-valued functions defined on edges dimension is always 2 ;) if you want higher dimension you have to consider higher-dimensional cliques beyond edges (that are 2-cliques): triangles, tetrahedra, and so on. But very often this is not necessary, even when discretizing 3d stuff. For example, for Poisson equation, and t…

That seems a weird choice of tangent space, surely a circle and a disk should have different tangent spaces?

And they do. When you discretize a circle as a graph, all the edges are along the boundary. When you discretize the disk, you fill its whole interior with edges in all directions.
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