NMCS4ALL: Optimization by Hillclimbing

[@MrBaconstrips13]

I could listen to this man talk about anything, forever.

[@tirthpandya]

"and yes its a cat, we are still on the Internet!".. lolll Amazing explanation. Very well articulated. Loved the examples and the comparisons.

[@davidasaf]

How is this not a more popular video for basic randomized optimization? Best explanation i've seen on youtube. Thank you.

[@joshcorney6666]

Ok....Why don't I have instructors this good. WELL done.... I'm in a Master's degree program and I was able to better understand hill climbing through this 18 min video vice a 4 hour lecture. Thank you!

[@DaveAckley]

Thanks for the comment!

[@devilsdante]

Well that makes 2 of us. Masters in computer engineering and this explanation was batter than any my teachers gave at the time of my classes.

[@jaysinhp]

Very nice.

[@stevenstefano8778]

Excellent video. Great combination of lecture and visual aids. The presentation helped me comprehend the topics better than reading from a textbook.

[@DaveAckley]

Hi Roshan, thanks for the feedback and for spreading the word! I'm only doing a few videos for NMCS4ALL, but I'm working on other video stuff as well. Hope to debut a new series by this summer; we shall see.

[@roshanram]

Dr. Ackley, Love this series. Please keep it going. In my long years at UNM, my biggest regret is that I didn't take a class with you. I am so glad you are doing this on KZbin now. Excellent material. I am getting everyone in my team at Amazon to watch it. The videos are very well produced as well!

[@GeraSanz]

by far the best video explanation of hill climbing! simple, to the point, with real time example!

[@DaveAckley]

+Gera “gsanz” Sanz Thanks for checking it out.

[@LukeSchoen]

1 minute in and im in love! Excellent knowledge

I love your videos! Found one about artificial life yesterday and today I was recommended this, and now I'm hooked. You explain things very well and do it in an entertaining way! Keep it up!

[@KurtMueller]

Great primer on hill climbing search. The animations and explanations helped a lot.

[@karimhamasni5482]

Amazing video! Explained in a great way that no textbook ever could match!

[@DaveAckley]

Karim H :) Thanks for the comment.

[@maersklandro]

Brilliant. Very glad I stumbled across your channel.

[@nickdai6420]

Awesome! Your explanation is really nice and clear.

[@BoJack32]

This video is phenomenal!

[@ermangurses1278]

Fantastic Lecture!!! Thanks a lot...

[@kermitblue7550]

I love you, Dr. Ackley!

[@emwinzy]

thanks dave, your explanation and demo are so awesome!

[@DaveAckley]

emwinzy Yay!

[@DarkRedman31]

Woaw amazing, you're talking very good, it's clear and consistent. P.S : What a pity a the end you didn't showed the annealing search versus the stochastic search ^^

[@JillPathak]

Thanks for excellent explanation!

[@progermv]

Nice video, thank you for sharing, Dr. Ackley!

[@garimamandal98]

This video helped me lot , very well explained

[@mistuhjit]

Fantastic video! Helping me for my algorithm's class a lot.

[@MrManMX]

Really cool video! Thanks for sharing this.

[@praveensubram]

Great one sir.

[@nteasushz]

Thank you for the great videos.

[@nitramcontrario]

Thank you Dave!

[@PunmasterSTP]

The explanations and visualizations were just top-notch! Thank you so much for making this video, and sharing everything else on your channel. Also, since Miley Cyrus's The Climb came out in 2009 and this video came out in 2011, did that song happen to cross your mind while making this video? 😀

[@carlinhosff]

This stuff help me a lot! Thanks!

[@pickleros]

awesome, I'm gonna watch all your vids

[@pickleros]

If I could tip you with dogecoin I'd totally do

[@DaveAckley]

Thanks for watching. /u/DaveAckley might conceivably work. Such modern.

[@matheox4425]

@@picklerosthis comment was very ahead of its time

[@DarkRedman31]

I don't know if it's the video or KZbin, I watch this video again and there is a lot of bleeping sounds so it's hard to heard the voice sometimes.

[@T1m0nat0r]

That Sir, was great!

[@JanRudolf1]

great video. Good job!

[@DaveAckley]

Thanks!

[@DarkRedman31]

Nice video, it's very interesting ! What are thoses softwares you use to test the three algorithms ? I at the end of video I was thinking "what a pity, I still want to see Simulated Annealing in work to compare it with the Stochastic that suceeds.

[@praveensubram]

Would be helpful if you provide the Data structures as well

[@markgalassi7627]

very nice explanation, but there is a clicking sound in the audio which is distracting; I wonder if it can be fixed

[@sfthe]

This is an excellent video!!! Thank you for posting this. I do have a question. When applying an optimization method to a new problem, it may not be clear how rough the space is. What is done in that situation? Is it worth trying to map out the space a little (for example, doing steepest climbing from several random starting points). Another question is what to do if the evaluation process is really time consuming and it is necessary to do as few evaluations as possible?

[@DaveAckley]

sfthe People try all sorts of things that sometimes help -- but there's no magic bullet for hard search problems.

[@mlittman]

Perfect!

[@dasnacl]

We're still on the internet :D

[@e13e7]

Nice!

[@gavinathling]

Great video, just a real shame about the audio quality.

[@laughtale1181]

its been educational

[@AlfonsoFR1978]

Thanks, I very much like your videos and the way you teach. You had a small mistake when speaking about the stochastic agent solving the masked image (around minute 16'): if the are 64 evaluations, the number of dimensions is actually 6, because 2^6=64.

[@DaveAckley]

Alfonso F R There are 64 masks, each of which can be included (1) or excluded (0) independently -- those are the 64 dimensions. The total size of the problem space is 2**64 or around 2e19. Thanks for the close watching and the comments!

[@AlfonsoFR1978]

I see. That makes 2^64, like about 16 and 18 zeros. Therefore, no more than one mask can be evaluated simultaneously?

[@DaveAckley]

Alfonso F R An evaluation can include any number of masks from 0 to all 64, but the algorithms in the video only consider changing the included/excluded status of one mask at a time.

[@AlfonsoFR1978]

***** Thank you for your quick and accurate answers. The identification of the concept of dimension with that of mask is not too trivial, as neither what the stop criterion is. While the stop criterion is obvious for a human (to see the cats), it's a bit unclear how the algorithm does determine if the picture has been completely decrypted. Further, It's quite a leap to go from 2 or 3 dimensions to 64 with nothing in between, because the next logical swivel would be to stack up pictures/masks in 3D (length, width, mask#). Then one also thinks about dimensionality reduction techniques, or whether Principal Component Analysis could be applied there prior to optimizing. Of course the video had to be synthetic, and that is one of its many virtues. Perhaps that would better be dealt with and clarified in another lecture, anyway. Keep those coming! :)

[@DaveAckley]

Alfonso F R The stochastic hillclimber does keep going after the picture is complete -- but everything it tries scores so poorly it's unlikely to pick them. Thanks again for the thoughts!

[@manueld4379]

Hello, I like your vids, especially this one. And I'd like to know if this software is available to anyone and if you have sources or docs for developpers to implement theses features in any languages. I'm willing to learn it and also neural network/genetic algorithm.

[@stumbling]

I hear a lot of popping and crackling from your mic (is it a lapel mic? I have heard lots of bad things about using those). Man, I hate audio! How is it we now have HD cameras that you just point and shoot but still it seems you must be a technician to get decent audio??

[@DaveAckley]

CowLunch Good mic, bad video processing workflow, at the time. The more recent ones are (mostly) better.

[@santhoshkumarstudycircle9115]

Great ...

[@benjaminweeg9684]

Nice :)

[@muhairwebesiimamichael3508]

how can this hill climbing algorithm be used in influence maximization optimization?

[@DaveAckley]

It's out of my area, but googling 'greedy influence maximization' finds lots of hits. Approaches described as 'greedy algorithms' _are_ hillclimbing.

[@muhairwebesiimamichael3508]

could you be having any reference on youtube and recommend

[@DaveAckley]

I have no such references for you.

[@wjrasmussen666]

Is it possible to get the source?

[@DaveAckley]

I don't give out the source for the demos because I just hack it up for my own use in the video, and I don't work it up to anything like distribution quality. Better to implement something new!

[@DaveAckley]

The code is all Java written for the video.

[@akdune]

Dr. Ackley, is the code listed on github? I

[@erepap]

[@izellets7361]

I am going to sound like an idiot, but the reason I click your thumbnails always seems to be that your beard looks like it is made of parentheses.

[@DaveAckley]

Hopefully they're balanced.