The Traveling Salesman Problem: When Good Enough Beats Perfect
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- Опубліковано 30 тра 2024
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The Traveling Salesman Problem (TSP) is one of the most notorious problems in all of computer science. In this video, we dive into why the problem presents such a challenge for computer scientists and some of the clever methods used to solve the problem.
We start with showing why all brute force solutions and even optimizations to get exact solutions can't reliably be used for large instances of the problem. We then proceed to discuss some heuristic based approaches such as nearest neighbors, greedy, and Christofides to get solutions that are reasonably close to the optimal solution.
But after finding a candidate solution, we also show how one might improve this solution via local search. We discuss some interesting algorithms for tour improvements including 2-opt, random swapping, and 3-opt improvements. Finally, we show some clever ways to analyze the search space, including simulated annealing and ant colony optimization.
Chapters:
0:00 Intro
1:27 Problem Definition
2:27 Why Finding Optimal Solution Is Practically Impossible
5:35 Nearest Neighbor Heuristic
6:59 Lower Bounding TSP
11:03 Greedy Heuristic
12:06 Christofides Algorithm
16:11 Sponsor (CuriosityStream)
17:15 Tour Improvements
21:13 Simulated Annealing
24:14 Ant Colony Optimization
28:25 Conclusion
Animations created jointly by Nipun Ramakrishnan and Jesús Rascón.
References:
Nice interactive on various TSP algorithms: cse442-17f.github.io/Travelin...
Many of the results for the algorithms are based on findings in this paper: www.cs.ubc.ca/~hutter/previou...
This video wouldn't be possible without the open source library manim created by 3blue1brown and maintained by Manim Community.
The Manim Community Developers. (2022). Manim - Mathematical Animation Framework (Version v0.11.0) [Computer software]. www.manim.community/
Here is link to the repository that contains the code used to generate the animations in this video: github.com/nipunramk/Reducible
Music in this video comes from Jesús Rascón and Aaskash Gandhi
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Can you do Max cut problem :)))
Congrats on the sponsorship dude!
Hello. I've academically researched the Travelling Salesman Problem for years now (mostly centered around finding better algorithms than existing branch-and-bound approaches). I can say you did quite a good job explaining it. Even better than I could do in some of my own presentations. If you're looking for a mind-bogglingly difficult problem, you could do one on the Bandwidth Minimization Problem. If I remember correctly one of the algorithms for it had complexity ≈ O(20^N).
What software do you use to create animations?
Tell me a good digital signal processing text book
I know the optimal algorithm for a graph with two nodes.
Lol
Believe it or not, I have an algorithm even for three vertices!
Prove it!
@@gemigames154 too long to write in a comment
@@cyanmargh i am such a genious that i may be able to solve 4 vertices graph!
"Sometimes things have to get worse before they get better."
I didn't expect life wisdom to come from a video on heuristic optimization algorithms. A surprise to be sure, but a welcome one.
Thanks to the greedy algorithm, humanity discovered fossil fuel.
TSP is a NP hard problem but so is making this video. Every seconds of the video I see an exponential effort invested. Thank you so much!
@@abdulhfhd ohh nooo why did you correct him? you didn't owe him anything
@@abdulhfhd fs bestie, remember what you like to say. you don't owe anyone anything
@@abdulhfhd yeah but you still don't owe anyone anything. remember that. thats your motto 😂
@@abdulhfhd 💦 don't give it up hunny
@@abdulhfhd ;p
In the '80's, I used a different strategy to solve this problem, just for my satisfaction. I based my solution on the way Nature would create an inner expanding bubble until touching all the nodes with minimal tensional energy. I used Basic in an 80286 PC, with about 100 random nodes. A short program with less than 100 lines of code was enough to get satisfactory results.
Edit: minor grammatical correction (English is not my first language, sorry for that).
This is cool idea!
Really clever!
@@viniciusfriasaleite8016 Obrigado.
@@kemsekov6331 Thank you.
I mean, a bubble tries to minimize tension around the outside, which is basically just a 3D version of the same problem.
I'm quite impressed that you managed to avoid mentioning entropy while discussing the annealing approach and the pheromone approach. I once tried to explain pheromone optimization to a friend of mine who wasn't familiar with using entropy to backtrack a search, and I simply wasn't able to explain the process. Well done!
Post Script:
My specialty is particle physics, and his is machine learning, so maybe I'm just too in love with entropy to shut up about it. (I have been told that it is not normal to use the Bose Statistics to explain how I organize eggs in a carton.)
Interesting, the entropy connection feels natural with Simulated Annealing, and it makes some sense with Ant Colony Optimization. I couldn't quite find an appropriate place to add it into the script without it feeling a little forced though. Thanks for bringing up this connection.
@@breckandolley8904 Even without having ever heard of Bose-Einstein statistics before, but knowing already it's an obscure branch of advanced maths, this made me laugh out loud. Nevertheless, I did check out what it is afterwards, so thanks for bringing it's existence to my attention.
@@herseem Bose-Einstein is like second year Physics undergraduate material, hardly arcane forbidden knowledge
@@TAP7a I believe you, and that is still pretty obscure for most people who struggle to understand basic statistics. And still far more advanced than is required for filling an egg carton
Been excited for this video for awhile. I’ve heard of TSP superficially many times.. but never got a deep dive like this before. Very motivated, exceptionally beautiful and naturally narrated. Content like this is the gem of UA-cam.
Just to add: As impossible as this problem is.. it’s wild to know we have a record solution with a tour length of 7.5+ billion.
there are harder problems
PSPACE problems, common in planning, are arguably "harder", as TSP is "only" NP-hard.
And then you have infinitely many degrees of uncomputable problems on top of that.
According to the time hierarchy theorem, there even are infinitely many *computable* classes harder than NP.
I think the video title is a bit misleading (or wrong really)
It also has a 3/2 approx in general and PTAS in the planar and euclidean cases. Further real world instances are often solved to within 1-2%
Assuming NP != PSPACE
@@Vaaaaadim i think that that is proven, but i am not sure.
but the Problem, if a program runs infinity, is unsolveable.
So I had this job at a water-selling firm (pumping artesian water source and distributing it either in bottles or through dispensing machines). I took care of machine maintenance and also the logistics of refilling the machines with water. Basically making routes for the drivers that did it. Then some time later got into coding trying to change my career, and in search of ideas to build a portfolio, had this "brilliant" idea of making a routing algorithm. "Should be easy, I can do the routes with my eyes closed by now, just need to put the logic in my brain into code". Or so I thought. After some research, you guessed it, it turned out that I was trying to tackle the TSP. After lots of hours trying to get it to output routes that actually make sense scaled it down to a small web-app that just sorted stuff by priorities, while still doing routing myself, still saved some time and made a nice entry into the portfolio.
Never would I have thought that there is a connection between crystallurgy and the travelling salesman problem. What a beautiful video. Thank you for making this accessible for everyone!
I have an MSc in Computing Science, but I have never seen a treatment of TSP and friends as beautiful as this. Thank you.
It's interesting to see how a "simplifyed" (two way edges, linear paths) can become arbitrarily hard to solve. Imagine bringing this problem to a urban scenario, where edges are streets that are not necessarily 2 way and are non linear and trafic is involved.
In a lot of cases that might be more difficult, but in many cases it might actually be easier to solve. Adding additional rules to the system could make most potential solutions very obviously bad or eliminate them entirely until there are only a few possibilities to check.
The ant solution might be the best in that case, and it would work without any extra modification. ( for one-way roads just initialize the appropriate entry in the weight matrix as zero instead of one )
Plus it can adapt to variable conditions well. ( Adding as an additional input an array that weighs the randomness on each node may help with sudden changes. )
Never knew there were so many similarities between solving TSPs and reinforcement learning!
Simulated annealing is so similar to the explore-exploit problem/bandits/epsilon-greedy and the ant colony optimization also reminds me so much of genetic algorithms and reinforcement learning😻
Cool video!!
I was thinking the same, Ant colony optimization reminded me of genetic algorithm.
I did a report in Linear Algebra discussing using Adjacency Matrices to find paths from any point to any other point by raising matrices to their power step by step. I managed to make it work for minimum hop count and shortest path between two points. Unfortunately it doesnt solve the TSP because it does not go back to it starting point.. only that it can hit every point. We had another idea of using something similar to what we called gravitic weighting to image creating the shortest path by picking any point and then "lifting it" and seeing the other points fall based upon distance.. we ran into problems with pruning of ineffecient routes.. if we could identify routes that were not going to be used we think it would have worked.. unfortunately our advisor didnt seem to care to help us at all... to him TSP was just an impossible problem and we were waisting our time.
Great insight on TSP. The research is vast and the information is often overwhelming.. but the way everything seems connected at the end is just amazing. Great intuitive ways to solve the TSP, would love to see more content on NP hard and NP complete problems. I started watching your content almost 2 years back when I first saw your ‘Towers of Hanoi’ video. Informative and concise.. love the animations
In love with the new intro. Amazing content as always! Doing computer science proud!
your videos are always a treat and often expose me to more knowledge that I didn't know exists before. thank you.
What an incredible intro to this problem. Wow, so clearly laid out. Thank you for all your work on this!!
Wow, that's a lot better than the 10 slides I got in university for this problem. Even took me less time to go through your video because it made so much more sense. Online universities with teachers like you would be so much better for everyone involved. Mad respect for you to make videos at this level of quality!
Woah this is a video that I was waiting for since a long time. I've attempted to solve many variations of TSPs but I couldn't find any good and intuitive video regarding TSPs and heuristic algorithms on UA-cam. Thank you so much!
This video was very well done! I come from a data science background, and so I was more familiar with the traveling salesman problem in the context of evolucionary optimization and multi objective optimization. The heuristic aproaches presented in this video are super interesting, and the presentation is amazing, it was really easy to understand.
Thank you - this was SO helpful. I watched it once this morning, and came back to rewatch and make notes in the afternoon, and I finally feel like I have a good understanding of the topic! Subscribed.
How to like a video multiple times? The explanation and animation was phenomenal. Great job!
This video is super well-made and really interesting, even as a person who already has some knowledge of the concepts mentioned. You did a great job taking a topic such as the TSP and simplifying it in a way that can be shown as an intro-level lesson.
The sheer amount of work that goes into creating these simulations.... Thank you!
Such an great video! You're doing an amazing job explaining those problems! Can't wait for your other videos!
This video is awesome. Great explanation and great montage (it's a lot of work to show all this nodes and highlight them)
Always great to see you post!
One of my favorite approximations is using a blob-like shape that stretches itself to just barely touch all the nodes while taking up as little space as possible, and then going around the edges of the blob.
The visualizations are just awesome and add so much to the already awesome explanation.
Fascinating and informative as usual. I love that you have the ability to make some rather difficult subjects understandable, even to my fine arts degree brain!
Incredible explanation! I learned about simmulated annealling in a stochastic simulation methods course but never got the key idea behind it, but thanks to you now I get it!
Very well done! TSP is one of the problems that interests me a lot but I just never got the time to learn it properly. This is the perfect introduction on TSP for all of us to enjoy :)
A perfect TSP 101
Absolutely top-quality video. Thanks for creating it. Has stimulated me to find an algorithmic solution that is better than what is currently available. Will be a useful problem for learning Python
I will definitely apply this to my thesis problem: Point cloud registration and 3D reconstruction. Thank you very much.
Sooooo well explained and animated, so good!
You did a great job on this video. I learned a lot and it solidified other understandings I had. Awesome!
I think you do amazing work, you have a talent at showing your passion for this beauty, it's truly infectious. Amazing work as always!
Great presentation on TSP, this was a pleasure to follow!
I loooove discrete optimization, very happy you made a video on it.
The problem with ACO as opposed to SA is that every single step of ACO is, in my (small) experience, slower. SA usually makes very local changes to the current solution, so calculating the makeup and cost of the next solution is very efficient, sometimes, like with 2-opt, even independent of size of the problem. ACO, on the other hand, has to compute its solutions from scratch, which makes it incredibly slow.
Thank you for this video, it has been a fine "survey" on TSP in aestheticaly pleasing and compact way.
Your narration has improved a lot! These keep getting better.
Great, very comprehensive overview with all the important details, learned a lot
Absolutely amazing video! Fantastic resources are posted in the information as well.
I was reading grokkings algorithms book last night and ended up looking for a video on TSP and didnt find anything good, then you uploaded this. Gotta love when that happens.
A wonderful video. I've tried to understand many of the algorithms mentioned in the video, but couldn't get it. Thanks to you I now see and understand the logic of those solutions! Thank you! :)
Quality of the Content is Mind Bending .
Great exploration of the problem space and amazing explanations of common approaches to solving it.
Awesome video! Nice summarisation of very complex ideas.
Phenomenal video. This deserves more views.
Anıther great video, as always.
This reminds me of my time trying to solve how to obtain the least amount of versions for a list of given version ranges and a set of available versions, for some reason. It operated by intersecting version ranges results together and taking the larger groups until all groups left didn't intersect.
Beautiful explanation of the Travelling Salesman Problem! 💛
I love this channel. This is better presented than a college education. Super high quality video. Thanks!
The production quality of your videos is insane. Keep it up!
Hello Lauren
Making this video must been a hell of a work. Thank you!
What an absolute banger of a video. Liked every second of it.
Such a high quality content! Thank you :) remembers me to the time where I learned that during studying
I had trouble programming a tic-tac-toe game where I didn't want to rely on simple strategies for the computer's moves. I wanted to create a logic function that could be expanded to 4x4, 5x5, or more grids. I gave up for the time, but plan to try again some day. I think this video is very informative and can give me some clues to creating the logic that I would like to create.
Great video! In my experience, greedy plus local search is a dead simple way to obtain very good solutions to many optimisation problems. To nitpick a little bit, TSP is not impossible, just intractable in its generality (for all we know).
Great video, simulated annealing is really interesting and easy to implement idea
Thank you for such a high quality video. It was interesting to watch and very informative. Thank you!
This is one of the greatest computer science videos I have ever seen. Wow.
this video was specially intuitive and engaging. Extremely entertaining presentation!
what an incredibly well done video mate
You're the only cs channel using manim at that level. Respect bro, keep going
Great intro to the TSP! You should check out the IPPA algorithm applied to this problem, it can create some great animations 😁
I'm a grad student studying this and honestly this is probably the best online explanation on this topic. 10/10 would watch again for reference
Amazingly easy to understand, thanks to you!
One idea: What if we initialize R in the AS algorithm to correspond to the MST? I think that would yield faster convergence to a good solution and/or better solutions on average.
Awesome explanations, thank you for the content!
Thank you for the amazing animations and clear explanation!
amazing video... thanks for clear and interesting visualization!
I was just reading about approximation algorithms in Kleinberg’s and Tardos’s ‘Algorithm Design’ book. Great job at succinctly and informatively presenting the topics!
Great video! Learned so much from this.
And yet in real life we (in Operations Research) solve such problems and harder ones such as vehicle routing daily, using solvers that are based on linear programming. Finding optimal solutions is very practical actually, it's mostly *theoretically* impossible, but the worst cases do not arise organically (you can construct them though).
Happy to see simulated annealing here. I improved the quality of an audio encoder I wrote that used vector quantization by introducing simulated annealing to refine the initial codebook.
A classical problem from the field of Operations Research and one the most studied
thank you for this reducible
Annealing also reminds me of stochastic descent. Find the direction that decreases what you're trying to minimize, then add some fuzz to help to escape local minima.
I always felt a bit daft at formal math but I'm amazed at how this video visualized my own intuition
Complexity Theorist here. Did you know you can solve this in polynomial time using parallel computing on exponential processors? You just try every route at the same time. Since we all have video cards with thousands of cores, it's mostly practical to use this method for small data sets.
Sweet, a new video! You're incredible.
Wow, I've been wanting to learn about this and take a crack at it myself!
This video was fascinating!
Alpha Pheonix's Voting Block algorithm was a beautiful demonstration of this
beautiful video, great moments in my degree studying this problem
Absolutely incredible stuff
That was really interesting, I love your channel :)
I remember simulated annealing from learning nonlinear numerical optimization - it's a popular way to discover local minima that wouldn't be otherwise found with systematic approach.
I had issue understanding your pronunciation of "accept", so I did a huge mistake and went searching for how to pronounce it!
I'm more confused now!
Anyway, great video.
it's pronounced "accept"
you're welcome
I didn't know that simulated annealing and ant colony optimization was useful in travelling salesmen problems! Very cool
I once coded a similar algorithm. My version started with a single point (the origin point) and then added the other points one by one, in no particular order, but for each point an existing section of the path was broken up and the point inserted into it, and it checked for each existing segment how much length would be added if the new point was inserted into that segment, and the segment with the shortest length increase would be chosen to insert the new point into. Don't know how good of a solution this was though... By the way, I used this for a simulation of a scooter service man employed by an e-scooter sharing company who had to service (collect, deploy or battery swap) several given scooters on the map, and it would give a list of directions, a series of coordinates where to go to and instructions of what to do there, and the coordinates were sorted by this algorithm.
I'm probably having a bias from the examples shown but I kept thinking that the optimal path is when you imagine a circular string that surrounds all the points starts shrinking onto the points. Ofc, this will only connect the outermost vertices and the inner ones will be left out. [Edit: So how do we get to the inner nodes from here? Let the strings grow until it hits one of the inner nodes. DO NOT RECURSE from here. Move to the next segment. Do this until all the inner nodes are visited] So I had another idea. Imagine making a polygon out of the vertices. I'm guessing that the polygon whose sum of external angles is the least will be the optimum solution. Well I could just be guessing.
Very nice video. Thanks for introducing the topic.
Coding Adventure did the ants colony so i was familiar but the simulated annealing is just so pretty as a natura approach to this problem, i want to try to implement these two as soon i've some spare time.
I am thinking that I saw a video where to calculate a lower bound was done by Linear Programming. Then the next task was to find a route which has this minimal cost. And from that video I remember that 100 nodes are paractically computable to get an optimal route.
It was nice to know, thanks
I had an idea for this problem at the start of the video,
Draw a shape that represents the average location of the remaining nodes before each, then choose between the closest nodes favoring nodes that are furthest from the average location border. I think it would help link those outer nodes so they don't have multiple long jumps to them
5:30 At one point we might get a good solution for the optimal road, if P =NP, then we will get it for large enough N anyway but even if not, there are some very fast solutions to many complex, NP-Complete problems. Like the N queens problem or the exact cover problem (or the generalised cover problem)
Such a nice video! I appreciate the fundamental approach to describe the TSP and showing various ideas to solve it. At the end I thought that it would be nice to hear a brief mention of Dynamic Programming (or at least Reinforcement Learning) for TSP.
A general solution to the Travelling Salesman problem for ANY graph:
1.) Throw away the graph.
2.) Create a single node.
3.) Since there is only one node, the solution for this graph is optimal.
This algorithm runs in O(1) time. I can't overstate how important this development is to the world.
This video is so good that I want more people to see it, so I'm commenting to drive engagement.