The simulated annealing algorithm explained with an analogy to a toy

[@kvin007]

Thanks, helped me a lot :)

[@samKh947]

1:07 Our boi hooked 5 rings on his first try , what a mad lad.

[@shortssquad1]

Bwahahahahahaha

[@Gunth0r]

I know, right. I'm not even interested in SA anymore. I want to see this pro beat the bubble toy again. I can't believe what happened.

[@AM-pq1rq]

I like how he seemed to momentarily compute if he should comment on it, but then went on with the main subject... move not accepted

[@nikosshadow]

After reading paper after paper, I clapped on this video...amazing! Thank you

[@lakdesuraj]

I clap your mom

[@noahglynn]

Thank you for providing an analogy to the toy. I am not a mathematician but you have explained it simply and creatively. Well done!

[@pierreauburtin2218]

The toy analogy is brillant. It really gives meaning to the algorithm.
Thanks a lot for this video !

[@roxymao5791]

This is literally the best video I have seen so far to explain the simulated annealing algorithm!!

[@hansergonzalez6646]

Using the toy as a way of explaining the topic was brilliant! So well explained, congrats!

[@sinaazartash3566]

Badri. I love how you think through every thought in the explanation.

[@usiahprax3667]

I am doing a udemy course and the guy is not given many examples. You tying it to the water bubble game made it easy to understand. Thank you.

[@aadithyahrudhay2269]

Thanks for the toy ring analogy, this is the first video where I actually understood the crux and beauty of the algorithm :)

[@darksideskyy]

Extremely clear explanation, step by step, you dont presume we know too much about the algorithm aside from basic equations (and lets be honest if you can’t do that, you shouldn’t be here 😅).
Thank you so much for sharing your wisdom 🙏

[@varshapatil3566]

Best explanation on Simulated Annealing

[@ericelegado1677]

Once again, another good example of a TH-cam video being better than my actual school.

[@simonchantack23]

Thanks, Badri for the clear and concise explanation. Your video was super helpful and much easier to understand than the books I have read. The water toy HELPED! Great artifact to use to make things simple.

[@Superdav06]

Thank you prof! this is my go to video for refreshing simulated annealing

[@UtaShirokage]

Thank you fot analyzing the algorithm, I finally understand how this idea works.

[@yoteango]

Thank you for putting subtitles. It helps me watch at 2x speed.

[@chinitaninoska]

The analogy is amazing! Thanks for the explanation

[@slimiabderrahmen4118]

Thank you for the creative way of explaining that.

[@MsTinyChiny]

Wow, very well done, thanks so much Mr Adhikari for the good explanation and great example!

[@janaSdj]

Example was the best to visualize this example. Thank you..

[@elinanikolopoulou1769]

Thank you for the clear explanation, you are a great teacher !!

[@eeriksp]

The ring game was a very helpful parallel, thank you!

[@charlesvictorio8101]

In the else if statement, there is a negative sign missing, it should be e ^ (-dE / T). This term represents the probability of accepting the new state, so it makes sense that e ^ (-dE / T) approaches 0 as dE increases and T decreases.

[@prokilz]

thanks dai, ekdam useful thiyo yo video, voli exam chha, this was great way to review this particular algo

[@Userisnmuh]

very well explanation..thank you 🙂🙃

[@mohamedelkony4780]

where did the probability role came from?

[@shahulrahman2516]

Thank you very much sir, for your efforts. Kindly make more such videos

[@daminglu6830]

Great explanation! Thanks Prof. Adhikari!

[@esmeraldalopez7]

Thanks! I had a game as the one in the example... I remember good childhood times!

[@suhanikashyap839]

Very well explained. Thank You!!

[@ProfASPatel]

Great Explanation sir.

[@prasundatta6590]

Good ,to the point,precise explanation

[@אוריתרוקח]

Thanks for the clear explanation!!!

[@arash_mehrabi]

Very nice explanation. Thank you!

[@dlerosman6183]

Very nice explanation.Thanks

[@weactweimpactcharityassoci3964]

its a good video but i didn't understand something in this annealing algo we started from T max to T min, how did u say its a maximization problem ?

[@naserjnealhouti5289]

I'm also confused on this aspect. What would be the maximum temperature for your ring game. would it be 373 Kelvin since that's the temperature you would need to boil the water ?

[@tomhas4442]

Very nice explanation. Many thanks

[@birajghosh3671]

Really well explained! The toy example was brilliant. And hill climbing was a bonus :)

[@luthfiyyahamari7464]

Jazakallahu khoiron jazaa'. Thank you so much.

[@sameerkhnl1]

Very well explained sir. Thank you.

[@cristel2481]

The only one who really could explain!

[@pallaviy9467]

Nice explanation thank you sir

[@bushraw66]

Thank you so much, very well explained.

[@abdullahmoiz8151]

Great explanation and analogy, Thank you very much

[@carlosarengas7875]

Thanks, Finally I can understand what it is about!

[@yasameenmohammed4366]

Thank you so much for this clear explanation!

[@ram-tv5701]

Thanks for the information badri!
One thing that I didn't understand is the repetition of the program , does it stop after the repetition of the epoch is 0 , or when the temperature reaches the minimum?

[@pranavpednekar7247]

Temp reaches minimum, because if you stop when ΔE = 0 then that will give you a local maxima and not a global maxima

[@nashadjuman3901]

Thank you - very clear explanation.

[@aymanshaawat]

Thanks
Very useful explanation

[@capeandcode]

Really good one! I was stuck on unclear explanations!

[@mbord7057]

Great explanation!! Thank you!!

[@TestAccount40404]

Best explanation out there, thank you so much :)

[@nicholaswalton4500]

Very good explanation

[@abyazkarim6755]

very clear explanation, thank you

[@kennyPAGC]

is the actual number e used to calculate the probability of picking a "bad" state? In other sources like Wikipedia I've seen that e refers to the energy of the state E(s), not to the number e. And to make the choice a probability function is used, based on delta E and T. Could anybody clarify this? Also I've no idea what probability function that is, can someone shed some light?

[@NewInfinityRecursion]

Thanks! When are generic algorithms a better option?

[@kshirasagarsahoo4254]

Nice video..easy to understand...

[@babakjan]

Really good explanation

[@abramswee]

thank you for explaining this SA algorithm. much appreciated.

[@utkarshthusoo9535]

Amazing explanation

[@AbhilashKorraprolu]

Beautifully explained. Didn't like the toy thing that much but the 2nd half of you explaining the alg was just perfection. Thank you so much.

[@dominiks7505]

Nice way of explaining, thanks a bunch!

[@AbdellahSaaouf]

Thank you! Clear and concise!

[@leonliu2079]

Thank you! It’s really clear and helpful. Is there any example that shows how we use the algorithm to solve real problem?

[@aakashsaiunnam1065]

Thank you sir, very helpful and interesting!

[@chanikanjenny273]

You explain very clear. Thank you!

[@rickytedja1965]

Thank you for explaining it so clear and efficient.

[@RaviShankar-jm1qw]

Very wonderfully explained :)

[@freelancemusician4634]

fantastic lecture even kids will understand simulated annealing kudos to you...I would like to talk to you

[@ashwanikumar4288]

Wow that was a great way of explaining the algo. Do u have a video on genetic algo alos?

[@rodo1]

10:20 Should be "Local Maxima".
Thanks for the content :)

[@jrf2623]

Thank you, very good explanation

[@brianc.3609]

Nice content!!👍

[@noorhadila38]

i feel confused, which is correct? Delta E >0 or Delta E

[@balanc2117]

Tempering and annealing both are same?

[@jiachengli608]

Vivid explanation, thanks!

[@AzfarImtiaz]

Excellently explained!

[@egedenizpekelalumni3099]

Thank you for the explanation. Helped a lot!!

[@danie745]

Very clear explanation, thanks!

[@khaledsalem1454]

Thank you
You are very good
can you tell what is a software can I use it to solve problems using simulated annealing and the tutorial of it

[@reactdev101]

Nice a very good explanation

[@changmuyan3383]

Thanks!! Helped me a lot.

[@arshadyusuf4803]

Thank you sir! Very good explanation!

[@abdullahkhalid9581]

Thanks Prof, Crystal Clear explanation :)

[@user-em9mw9ch3y]

10:20 I think you mean 'Local Maxima' ?

[@patriot524]

That explanation was awesome. Thank you so much!!!!

[@sablezubshruz9811]

Very good, clean, simple explanation. Probably best I've seen.

[@sarthakmishra6915]

explained very well dai

[@ManasbiParajuli]

sarthak mishra waow.

[@ashweenasundar]

Sir, how to determine the maximum temperature?

[@iam_sangohan6047]

how defines the 'next' function ?

[@AgilYolch]

Hi, thank you for the video. I would like to know how you calculate state energy? I mean with which way you calculate each epoch energy for E(n) (Also E(c) for the initial state). How the temperature change at each iteration? I mean T(max) is as you said 3000 in the initial state. How I can calculate the second state temperature. Thank you for reading.​

[@BadriAdhikari1]

Agil Yolchuyev calculating energy E totally depends on your problem. For eg, when calculating shortest paths, sum of the distances could be your energy.

[@BadriAdhikari1]

Agil Yolchuyev for temperature try linear decrease like T = T -1 first. Then try other monotonic decreasing functions.

[@gauravnagrale185]

Algorithm is well explained, thanks a lot!!

[@chewjingwei2635]

Genius explanation!

[@adelsakkir6183]

Good Explanation!

[@destinyjames6117]

Thank you very much for your help kind sir ^_^ You helped me out a lot.

[@sebyvarghese874]

very good explanation

[@ibrahimcetin153]

I think that it is very good explanation I have ever seen

[@tknaveen4]

What do you set the max temperature as?

[@muhammedfuatnuroglu3025]

Really thank you.
Great explanation.