James Webb Space Telescope and the Traveling Salesman Problem

How do you decide which stars the world's most expensive telescope looks at? This video introduces the Traveling Salesman Problem and some basic algorithms to solve it, attitude control and momentum management on the James Webb Space Telescope, and some key concepts of all optimization problems.
Become a Patreon member: / physicsforthebirds
0:00 Intro
1:36 The Traveling Salesman Problem
6:08 Webb Momentum Management
9:41 Using the Universe as a Computer
Thank you to Caleb Birtwistle for captioning!
Circuit Board Drilling: www.zib.de/groetschel/pubnew/...
Let the Pigeon Drive the Bus: pubmed.ncbi.nlm.nih.gov/21965...
JWST Solar Panels: svs.gsfc.nasa.gov/13208
JWST Heat Shield: www.nasa.gov/content/goddard/...
JWST Momentum Overview: jwst-docs.stsci.edu/jwst-obse...
JWST Observation Planning:
www.stsci.edu/largefiles/iwps...
www.aaai.org/Papers/ICAPS/200...
citeseerx.ist.psu.edu/documen...
robotics.estec.esa.int/i-SAIRA...

[@badguy562]

I am stunned how deep the problem goes and the parallels of it to others. I liked the way you introduced the problem by an familiar question of shortest path.

[@paulallenscards]

This is a problem that Amazon has been trying to solve for the better part of 15 years now

[@Quadr44t]

Totally, I first was thinking of molecular dynamics/Monte Carlo simulations. Then obviously self learning algorithms..... And I think, tho it wasn't at all mentioned here, that I finally understand why Quantum Computing could have so much potential for protein folding. And, well, organic chemistry/supramolecular chemistry/material science in general. There is a pretty obvious variable that needs minimizing. Minimize conformational (Gibbs?) free energy (and maybe occasionally ramp up temperature to 20! to avoid those nasty fake minima). Edit: This is the beauty of physics, tho. The more you learn, the more you see underlying concepts/mechanisms repeat like everywhere. It is ridiculous how much the concept of the euler unit circle comes up. Like, everything can be described by motion around a unit circle (or the 1D projection thereof, i.e. waves). Didn't expect this knowledge to be this damn useful in a hobby like music production (like in practice), but man, Fourier transforms and wave-dynamics really make a lot of (more technical) music production concepts super intuitive. But I digress

[@havenbastion]

It's not that deep. Certainty is always and only sufficient for a given use case, as proven by the fact that when you're certain enough to accept the fact or take the action there is no further reason to gather more information.

[@Quadr44t]

@@havenbastion That's true. And when you dive deeper anyway, you realise that you never really understood it in the first place. (which never ends, you can always go deeper)

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I really hope you keep this up, your videos are SO AWESOME

[@physicsforthebirds]

I'm glad you liked it! It's fun for me, so there's no reason not to keep it up

[@CircuitrinosOfficial]

A correction at 3:00 Because the routes are always a complete cycle, it doesn't actually matter which point you start at. The actual equation for the number of possible unique routes is R(N) = (N-1)! The equation you used is overcounting the possible routes by a factor of N. Another way to look at it is you want to count the possible connections between cities and not the cities themselves.

[@cameronreid8383]

Seems like he missed that detail in the whole video. I'm not sure the TSP even applies to JWST, since afaik there's no requirement to return to the start point, and there is a designated start point

[@CircuitrinosOfficial]

@@cameronreid8383 Yeah, it technically isn't the traveling salesman problem, it's the Hamiltonian Path. In that case it does matter where you start.

[@DemPilafian]

This particular salesperson is young, unmarried, travels light, and has no permanent home.

[@sagoot]

Because it isn't a complete cycle... you don't go back from the last point yo the first

[@CircuitrinosOfficial]

@@sagoot Then it isn't the traveling salesman problem.

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Ikr

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I'm a machine learning researcher by trade who specializes in multi-objective optimizations. This was a great explanation and so enjoyable to watch, thanks for the content! I'm subscribed now

[@jamesphillips2285]

3:00 Well played. Once was trying to solve a visual challenge where you are supposed to draw a line crossing every line once. I Decided to try simplifying it before throwing a week of CPU time at it. Once I was able to simplify the problem to a pentagon: I was able to prove it was impossible. Due to the odd number of sides any line starting inside will end up outside after crossing each side once. Since the figure I transformed in simplification had 3 pentagons, but the line has 2 ends: no solution is possible.

[@Xoque551]

You might have stumbled on the Planarity Problem! Your graph contains a K_5 complete Graph or a K_{3,3} complete bipartite graph if-and-only-if your graph is not planar. And thus, you can't ever totally eliminate line-crossing (at least not in the Euclidean Plane).

[@codiserville593]

I knew of a visual problem that sounds similar where you had to do I think the same. Cross every line in the shape but only once and see if it was possible or impossible

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@@codiserville593 I think the "gag" is that it is impossible, but that there are so many possibilities that it can keep you occupied for years. I think I proved it impossible after 2 years.

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@@jamesphillips2285 yeah sounds like it. See when it was my friend who showed me the problem he told me. " In high school our teacher showed it to us. He never explained if it was possible or not"

[@jamesphillips2285]

@@codiserville593 The version I was talking about was 3 horizontal lines with the top divided into 2 boxes and the bottom into 3 boxes. My initial simplification was proving that I could cut the corners off of the 3 box row. I then realized I could distort the remaining shape into 3 conjoined pentagons.

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[@kasuha]

In fact, even nature does not "know" the solutions. It's getting stuck in local minima all the time. For example, most stable configuration of elementary particles is Iron or Nickel, yet most of the visible matter in the universe is in the form of Hydrogen, local minimum that is pretty far from the global minimum. Our whole existence is thanks to that "geetting stuck in local minima" principle, not just atoms of our bodies but the whole chemistry, biology, and evolution.

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Loved the piece on multi objective optimization and your explanation of attitude control for jwst was spot on :)

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