81–90 of 172 posts
Any physics experts know what the actual optimal shape is? I imagine it would be a pretty gnarly problem to work out.
Well, IIRC, theatrical lights use parabolic reflectors to focus a non-point source into a point on a lens, so that might be a good place to start.
Really impressed with the use of Monte Carlo method. A while back I ran into some resistance trying to advocate simpler statistical methods to solve a problem domain similar to this, while the team in question repeatedly wanted to reach for a machine learning solution. I'd love to know if I was wrong here. In my mind, when an algorithmic or heuristic path to a solution is available, we should attempt it first before…
Really impressed with the use of Monte Carlo method. A while back I ran into some resistance trying to advocate simpler statistical methods to solve a problem domain similar to this, while the team in question repeatedly wanted to reach for a machine learning solution. I'd love to know if I was wrong here. In my mind, when an algorithmic or heuristic path to a solution is available, we should attempt it first before…
A common trap in tech is to reach for the fanciest tool instead of the simplest. The best engineering often comes from mastery of the simple tools, and the most beautiful engineering is the one that makes you say 'that's so obvious, why didn't anyone ever come up with that before?'
So I wrote a very simple python script that would randomly generate layouts of who would sit in each room and next to whom. Every time it gave me a result I scanned it for conditions that would make it not work and add a rule to skip such configurations. After about six such edits I got a layout I thought was acceptable. The team as far as I know was happy and nobody questioned it for the entire time we were there. This saved me time because I didn’t have to pre-program all the conditions, only add ones I had already seen not work. Saved both CPU and brain cycles, so to speak.
Really impressed with the use of Monte Carlo method. A while back I ran into some resistance trying to advocate simpler statistical methods to solve a problem domain similar to this, while the team in question repeatedly wanted to reach for a machine learning solution. I'd love to know if I was wrong here. In my mind, when an algorithmic or heuristic path to a solution is available, we should attempt it first before…
A common trap in tech is to reach for the fanciest tool instead of the simplest. The best engineering often comes from mastery of the simple tools, and the most beautiful engineering is the one that makes you say 'that's so obvious, why didn't anyone ever come up with that before?'
Many times people have specifically researched why a certain method is better than status quo in practice, on real world data with real world operating constraints.
A good example is the paper “Let’s Put the Garbage-Can Regressions and Garbage-Can Probits Where They Belong” - explain an extremely common and hugely severe problem with “garbage can” regression models.
Does this only work if you’re exactly aligned in front of the hoop? (eg Free-Throw Line)
Nope, expected something interesting, but this is based on a boring brute-force simulation. He did hint at using a parabolic shape, like in satellite dishes. Why not use some actual calculus and differential equations to figure out the optimal parabolic-like shape here?
Can someone briefly eli5 how the Monte Carlo simulations feed into learning the optimal surface?
Earlier quoted context omitted.
If you fix the position of the 'thrower' and just throw them at ridiculous speeds so gravity doesn't matter you'd end up with a hyperbolic backboard. So annoyingly for an 'optimal' solution you'd need to specify where people can throw from and how fast. Frankly you might as well just use a hyperbola with one foci on the hoop and the other on the middle of the court, or maybe a point slightly higher than the court its…
Wouldn't you rather want an ellipsoid with the thrower at one focus and the hoop at the other?