Max Flow Ford Fulkerson | Network Flow | Graph Theory

[@sixtenmuller8823]

Watching this in 2023 for my exam in Algorithm and Datastructures, love the explanation

[@Iam_number_one]

where do you study bro ?

[@UndercoverDog]

​In so sure hes also German haha ​@@Iam_number_one

[@sovathanaheng6673]

You spent less time than my Prof to explain this and you nailed it. Awesome!

[@yoavmor9002]

My prof is so old and senile I swear to God he was out there helping Alan Turing crack the enigma back in the day

[@kaiyueguo1624]

@@yoavmor9002 lol

[@gaurishgangwar]

Sometimes it is also because at this point you already have some idea of algorithm and understand some of its key concepts, so when William teaches you this you have an easier time grasping it.

[@siuhangchen8986]

I would thank my professor explaining the algorithm so terriblely otherwise I won't find this awesome channel!

[@harshitshukla36]

My thoughts when I first saw this algorithm 4:17 - 4:23

[@sagebaram5951]

Literally said it out-loud in zoom class last week ... accidentally.

[@weaponkid1121]

@Archer Grayson lmao u really made two accounts for this

[@famefurqan5937]

I wonder why this video is not highlighted in the search results. This is a wonderful video with effective teaching. You have done a good job William. Please Keep it going for lot more people to benefit!

[@ZantierTasa]

Good video, but there's a few things that could be improved. Residual edge is incorrectly defined at 6:00. At 4:20 he says you're probably wondering what a residual graph is, and then he proceeds to use the word residual before defining it! In Ford-Fulkerson, we assume each edge has an edge in the opposite direction. Where the capacity is only given in 1 direction, set the other direction's capacity to 0. Assign a flow of 0 to each edge, and then as the algorithm proceeds, the flow on each edge can be changed. But when you change the flow of an edge to x, you MUST assign -x to the flow in the opposite direction. This is NOT called a "residual". The residual exists in both directions. The residual (which means "quantity remaining") of an edge is what at 7:29 he calls "remaining capacity". It is simply the capacity of the edge minus the current flow. i.e. If the residual is > 0, there is still room for more flow. The residual graph is just the whole set of residual edges. The word saturate in general can mean "fill until no more can be held". In this case, a saturated edge has residual == 0.

[@kaierliang]

thank you!

[@Uthalerebaba-nr4qi]

Watching this in 2024 for my exam in Algorithms, love the explanation

[@hidayatkhan412]

This is the best algorithm channel on youtube, thank you for this video.

[@salmanfariss]

Amazing explanation and I love the animation as well! I think it is worth pointing out that the key observation that the sum of the bottlenecks (found in each augmenting path) equals the max flow is only true because we assumed the initial arc flow is 0 for each arc. If we do not assume this, then you need to add the initial value of the flow (of the graph) to the sum of bottlenecks as well.

[@makii1715]

Best explanation!! I watched some videos but this one was really in detail, giving examples and again repeating what a risidual network acually is, how capacities work and the principle of augmenting paths. Thanks a bunch my assignment was even fun when I understood it thanks to you~

[@ericandrade3510]

Love your network flow playlist, getting ready to watch it all again for the 2nd time. Great videos :D

[@v0nnyboy]

Amazing as always !! The impact these videos will have over time will be phenomenal ... Let my comment bear testimony to that prediction ...

[@WilliamFiset-videos]

Thank you!

[@navdeepsandhu7021]

Agreed

[@elisabethwaldron3192]

Only video that explains the concepts clearly without confusing the mess out of you🤣

[@yuhao8430]

This is so awesome!! Thank you! How come people dislike it?? 18 university prof?

[@DzmitryKashlach]

Thanks William, your videos have already helped to get Tarjan's algorithm and this video is super clear, as well! ;-)

[@beibeixiong5660]

Your explanation is so clear! Also, I love the illustrations. Keep up the good work!

[@yaelyacobovich397]

Best series on youtube yet

[@--..__]

this is so clear. my professors explanation was over an hour & made no sense. and you didn't oversimplify either! cheers

[@sumowll8903]

Really well done! Best video for Max flow i have seen so far!

[@adarshkishore6666]

Great work on this! One of the clearest explanations I have seen

[@adarshkishore6666]

Can you make one video on Push-Relabel algorithm also?

[@tritruong9352]

Very nice explanation Very clear I love this so much

[@rorywilliams7375]

i have an exam in an hour , i just love u

[@gergeerew6811]

Maybe when people learn theses concepts by themselves rather than being forced by the college it will be easier for them to understand. Anyway this video is awesome. Brief and clear. Thank you so much.

[@mukul3159]

But what is a residual graph? You went straight to augmenting path.

[@robin6091]

wached 3 videos before that didn't help me, I'm glad you did

[@jordanf3273]

Appreciate the videos! I'm in an operations research course and it's hard finding clips on some of these algorithms!

[@kells9k]

If Timotheus' pole has a network flow capacity of 8 units spunk along its edge, delta net, how much bottleneck maximum flow can Timotheus' hole take before it fissures?

[@tttthomas]

very good and clear video! I can now understand what is going on in the reverse operation!

[@yungbando01]

Thanks William, that was the best video i have seen for Residual Graphs and Augmenting paths, and trust me i saw many. You managed to make me undestand what my professor's couldn't in 100 silides.

[@mere_illusion]

someone give this man a GOLD......wait this isn't reddit

[@xbz24]

love your stuff, now I need to rewatch it to sink it xd

[@educationcurve8981]

teacher thanks. Muchas gracias con lo poco que entiendo ingles me ayudaste gracias profe.

[@hymnish_you]

Great Explanation

[@longflame4414]

This is beautiful, thank you so much!

[@_julian]

Great explanation!

[@sharmaanuj334]

Residual edges is more like an undo In augmenting paths, we can only select the path whose remaining capacity is greater than zero

[@stelioskossieris4530]

Thank you for your helpful explanation!!!

[@jenweatherwax7113]

I got totally lost around 4:47 when he starts talking about edges being saturated...? And what is a residual graph? And I don't remember depth first search... What if there are two bottlenecks in the graph?

[@ZantierTasa]

Good video, but there's a few things that could be improved. Residual edge is incorrectly defined at 6:00. At 4:20 he says you're probably wondering what a residual graph is, and then he proceeds to use the word residual before defining it! In Ford-Fulkerson, we assume each edge has an edge in the opposite direction. Where the capacity is only given in 1 direction, set the other direction's capacity to 0. Assign a flow of 0 to each edge, and then as the algorithm proceeds, the flow on each edge can be changed. But when you change the flow of an edge to x, you MUST assign -x to the flow in the opposite direction. This is NOT called a "residual". The residual exists in both directions. The residual (which means "quantity remaining") of an edge is what at 7:29 he calls "remaining capacity". It is simply the capacity of the edge minus the current flow. i.e. If the residual is > 0, there is still room for more flow. The residual graph is just the whole set of residual edges. The word saturate in general can mean "fill until no more can be held". In this case, a saturated edge has residual == 0.

[@zekaini7617]

best youtube university

[@alimahmoud1441]

Awesome video man, life-saving. Very well explained

[@jaincoral25]

how did the 4th/last augmented path had bottleneck value - 6?

[@ytec100]

Best explanation. Thanks!

[@man-kityau21]

Is there a particular reason why the "maximum flow" calculated using the "sum of bottleneck values"? Instead of using the "sum of outflow from the source node"? I found the "sum of outflow from the source node" easier to interpret, that's all.

[@Garentei]

The bottleneck approach is more useful to understand min-cut. Looking at the capacities that are actually filled gives you more information on how to reconstruct the optimal answer than looking at the outflow from the source. Sometimes, designing network flows is also easier to come about if you think about it in terms of min-cut.

[@paretodeficiente9586]

I have a doubt: why we have to compute the augmenting path (s -> 0 -> 3 -> t), if the arrow goes from 3 to 0 (3 -> 0), and not from 0 to 3 (0 -> 3)? Thanks.

[@ahlahous8128]

He said at ( 9:09 )that we can use the residual edge, we have created. So basically we used the residual edge 0->3

[@paretodeficiente9586]

@@ahlahous8128 Thanks!

[@thomasip9938]

Awesome introduction, thanks!

[@ShalokSharma-f2z]

does dfs really choose edges in random order

[@diskokaktus]

great video! keep it up :D

[@aishwaryashrestha798]

which tool do u use for animation ?

[@peterulev5202]

Very useful. Makes good sense! :D

[@anandiyer_iitm]

Not for someone who's trying to understand this for the first time. If one already knows, animations are nice.

[@osmankhalid2005]

From what time the F-F algo actually starts on example?

[@tjalferes]

Thank you.

[@SG-kn2jl]

Thanks mate appreciate it

[@viralforeverr]

thank you

[@ShakrinJahanMozumder]

Thanks!

[@kells9k]

great explanation. thanks for the video and quality explanation with the visualizations!

[@UdayTejaOriginal]

Could you explain how you came at 2/100 at 11:15 ?

[@noelnakka1056]

Can you please make videon busacker and gowen method for network flow

[@milindprajapat7177]

What about push relabel. Its not in the playlist!!

[@arunkanjoor6542]

You are the guy from data structures crash course by freecodecamp right??

[@bipinchowdary5251]

At 2:42, the max flow is 7. But I am not able to get any min-cut of value 7, the least I cab generate is 8. Can anyone find a mincut?

[@lucianoinso]

You sound a bit like Ross Geller from time to time lol! Great material tho, much appreciated hah!

[@narendraparmar1631]

thanks sir 😊

[@boehan605]

was this video supposed to be revision or first time learning? bc if it was for learning it was terrible cant lie

[@WilliamFiset-videos]

What is missing? Anything I can clarify?

[@boehan605]

@@WilliamFiset-videos you skimmed past probably the most confusing part of all of this: residual flow

[@ssshukla26]

Can you please teach the same thing to our professor who is teaching us the network flow. He is trying to explain the same thing to us from last 3 lectures and out of 250 not even 25 of us is able to understand the same.

[@ganeshchelluboyina]

kaunsa clg me padhte ho bhai

[@gabrielpereiramendes3463]

#Excelent!

[@zitronensaft9110]

if it weren't for youtube, i wouldn't be graduating college

[@vijaykumarsah6255]

He took an augmenting path and augmented all over the place

[@siminthesky]

we miss one or 2. checkout hieros gamos..

[@EmapMe]

Why not just do the opposite of a shortest path algorithm? wouldn't that give the maximum flow?

[@kroposman2302]

Great video, shitty algorithm

[@EL-KHABIR]

Algorithme Ford-Fulkerson (Flot maximal) Méthode marquage kzbin.info/www/bejne/j5-uiX13rbp0q5Y

[@ahmetnecipgorgulu4133]

I WANNA KISS THIS MAN HOLY FUCK THIS SAVED ME A LOT OF TIME

[@Poutoksereiseimaigw]

εσπασες μας

[@sybks]

11:34

[@wirito]

Hold on....isn’t this the guy from 3b1b?

[@sourabhkandari80]

8:08 😑😑

[@blade661]

Useless explanation

[@Gabriel-yk4it]

i dont understand shit

[@cem9927]

poor explanation ! make an example and solve it instead of bla bla

[@jameskaiser7474]

i now understand it less after watching, f***....

[@weaponkid1121]

makes no sense

[@saurabhvishwakarma4827]

voice is copy of bill gates

[@ljupcetrninkov4602]

i dont get it dude you explain too fast cant keep up disliked

[@ananyamenon8839]

terrible explanation, not clear at all

[@lucasmertens1625]

There is a lot good work in this video. At least give a hint on what is not clear to you. I think if you have your display and sound on, it is easy to follow and well explained

[@GANDHIXtv]

Good try but wrong information in some parts.

[@saulsalvadorhernandez9381]

thank you

[@밑에시간누르면광고없]

13:25