Thank you for your patience and hard work for actually walking us through the recursive process. The recursion tree is clear and solid. You really know what you are teaching unlike most of the LeetCode TH-camr out there who only copy and paste code. Thanks again!
I cannot thank you more. This is actually an interview question asked by Google. You made it look like very simple with your explanation. Tons of thank you, man!
You are actually explaining how to approach this problem unlike most of the leetcode youtuber( they just memorize the code and type it out). Thank you very much.
This is the best recursive tree video I have seen so far.Thank you so much SIR,now I can eat something,since I got it down.I was struggling to understand it for over a day.Thanks a million.
It's best ever!! Thank you so much for explaining step by step patiently lol. just hit subscribe but saw your latest update was 8 months ago, it would be great if you can keep going, your explanation is straightforward and super easy to understand, thank you!
this is the best illustration for recursion tree when called under a loop, every video just draw a two way or three way branch from beginning but this was exactly step by step , thnx
Today marks 3 years since you posted this video. Why have you stopped posting new videos? You are a very good teacher, Sir. You have a divine gift. Recursive thanks to you. PLEASE START POSTING NEW VIDEOS.
it was a piece of cake for you. And now for others too. Holy moly how well you explained, i cant describe it words, it was way to easy for us to understand what's going on. Thanks a ton mate. Cheers :)
@@mistercorea How does splice work internally? How do you say that splice is an O(1) algorithm? Splice itself cannot be O(1) but at the minimum has to be O(n).
Read the way of solving it using GeeksForGeeks, AlgoExpert, Leetcode prime solution explanation, couple of youtube videos. Didn't understand it from any, but you. Thank you.
Wow! You did a phenomenal job of explaining this in much simpler terms. Thank you so much for making this. Now, I understand this problem way better. I have one question though - Isn't the time complexity O(n * n!) from the tree diagram you illustrated? n times we are doing n! right, or can we reduce O(n * n!) to O(n!)?
You said we didn't use extra space in the video, but I think newNums is an extra space in each call. Space complexity will O(n*k) where n is the depth of tree and k is the extra space in each call. Please clarify.
we don't need to create a new list every time. We can use :- def permute(self, nums: List[int]) -> List[List[int]]: n = len(nums) ans = [] def rec(path): if not len(nums): ans.append(path[:]) return for i in nums[::-1]: nums.remove(i) path.append(i) rec(path) path.pop() nums.append(i) rec([]) return ans
These things always make sense in hindsight once it's been explained, but do normal people actually come up with these crazy-ass solutions on their own?? Need to figure out what to take away from this conceptually so I actually learn something instead of just being able to do this exact problem if I see it again.
I guess this is how it works. Problem solving is all about reading the pattern, the more problems you solve the faster you can come up with an idea what algorithm you need to apply. You can say that we are memorizing it, but who isn't? That's why "Practice makes perfect" is a thing. I know I probably never gonna use it again when really working, unless I am on an algorithm research team, but this is how the game is played.
I am really curios about what is really going on behind the scenes , for example when for the first time Permumation returns ,the next line set.pop is executed. But what was the next step? did it return back to the previous function call in the callStack( which seems it is) or did it just increased the i in for loop?
IN line 6 why are we doing this : answers.push([...set]) and why not simply this : answers.push(set)? set is an array and we can simply push the array into the answers array ?
Hi. I've tried to make sense of this for a long time but I am still confused. Because the base case (the if statement) is checked for before the nums array is filtered, shouldn't there be one more step of the tree for just pushing the array into the answers. In the tree showed in the video, the last step does not meet the base case because nums is not empty when the base case is checked (nums === [3]). Could someone please clarify?
I used Java to write this code. But I get something wrong, the result would be [1,2,3] only. What's the problem with my code here? And how to write it correctly in Java? public List permute(int[] nums) { if (nums == null || nums.length == 0) return new ArrayList(); List result = new ArrayList(); List list = new ArrayList(); for (int num: nums) list.add(num); List todo = new ArrayList(); backtrack(list, todo, result); return result; } private void backtrack(List list, List todo, List result) { if (list.size() == 0) { result.add(new ArrayList(todo)); } for (int i = 0; i < list.size(); i++) { todo.add(list.get(i)); list.remove(i); backtrack(list,todo,result); todo.remove(todo.size() - 1); } } Thanks a lot!
Good explanation but im not sure if its the best implementation in code? I've seen this problem solved using 2 functions with different # of parameters, what are the pros and cons of both implementations?
I used general backtracking logic to implement this. I don't know if its similar to what he is implementing as I only watched the logic. class Solution { List list=new ArrayList(); public List permute(int[] nums) { backtrack(new ArrayList(),nums); return list; } public void backtrack(List curr,int[] nums) { if(curr.size()==nums.length) { list.add(new ArrayList(curr)); } for(int i=0;i
This is the best recursive tree explanation I ever seen. Thank you
Thank you for your patience and hard work for actually walking us through the recursive process. The recursion tree is clear and solid. You really know what you are teaching unlike most of the LeetCode TH-camr out there who only copy and paste code. Thanks again!
I cannot thank you more. This is actually an interview question asked by Google. You made it look like very simple with your explanation. Tons of thank you, man!
everything is simple with good approach
That was the only video that helped me to really understand what happens in a backtracking solution.
Thank you sir
This was the first explanation of the Permutations problem I've found that makes sense. Thank you.
answers.push([...set]) instead of this : answers.push(set) solves the problem.
Can’t agree more!!!
You are actually explaining how to approach this problem unlike most of the leetcode youtuber( they just memorize the code and type it out). Thank you very much.
This is the best recursive tree video I have seen so far.Thank you so much SIR,now I can eat something,since I got it down.I was struggling to understand it for over a day.Thanks a million.
No one cared to explain how recursion is working inside the for loop. But you did it thanks a lot man!!
This is one of the best explanation of recursion I have come across, please keep up the good work.
It's best ever!! Thank you so much for explaining step by step patiently lol. just hit subscribe but saw your latest update was 8 months ago, it would be great if you can keep going, your explanation is straightforward and super easy to understand, thank you!
this is the best illustration for recursion tree when called under a loop, every video just draw a two way or three way branch from beginning but this was exactly step by step , thnx
please start posting new videos, I watched loads of algothrim videos, yours are the ones that make more senses. and easier to understand, Thank you~~~
Thank andi guru garu, ee logic ardham kaaka oka roju antha chacchi poya!
Today marks 3 years since you posted this video. Why have you stopped posting new videos? You are a very good teacher, Sir. You have a divine gift. Recursive thanks to you.
PLEASE START POSTING NEW VIDEOS.
it was a piece of cake for you.
And now for others too. Holy moly how well you explained, i cant describe it words, it was way to easy for us to understand what's going on.
Thanks a ton mate. Cheers :)
Great solution and thinking, thanks!
Time Complexity Infinity is a great concept , great channel and some brilliant work here
This is it! I found the best recursion explanation. Good one 👍
way cleaner solution than whats on leetcode rn. good stuff man. thanks
dude, u have the best explanation for this question
One of the best videos on recursion. Thanks!
Man you are a life saver !
Your use of filter to remove an element at an index is awesome. I've been using splice() with concat() but filter() is so much more elegant.
splice should be faster than the filter.
A very good explanation. I had to watch it 5 times though lol. Thank you!
Wonderful recursive explanation !!! Thank you.
Beautiful explanation! Been looking for something like this for a while now!!!
This video is very well done. Good Job. Man!
great explanation !
the removal of one element from an array itself is an O(n) complexity. That turns your solution into O(n*n*(n!))
What if you use a splice() function that will be O(1)? can you explain why it is O(n^2 * n!)?
@@mistercorea How does splice work internally? How do you say that splice is an O(1) algorithm? Splice itself cannot be O(1) but at the minimum has to be O(n).
Nice explanation!
Read the way of solving it using GeeksForGeeks, AlgoExpert, Leetcode prime solution explanation, couple of youtube videos.
Didn't understand it from any, but you.
Thank you.
Wow! You did a phenomenal job of explaining this in much simpler terms. Thank you so much for making this. Now, I understand this problem way better.
I have one question though - Isn't the time complexity O(n * n!) from the tree diagram you illustrated? n times we are doing n! right, or can we reduce O(n * n!) to O(n!)?
Best explanation!!
Please add more LeetCode challenges.. your explanations are awesome. Thank you.
Explained in such an easy manner. Thanks!
the best
The great explanation about tree and recursion.
Love you bro
You said we didn't use extra space in the video, but I think newNums is an extra space in each call. Space complexity will O(n*k) where n is the depth of tree and k is the extra space in each call. Please clarify.
Thanks for the clear explanation. This is the best explanation.
Brilliant explaination. Please do more videos
best explanation so far
Finally I get it!! thank you king
Thanks :)
very helpful recursion tree visualization
Great video!
you are best teacher so far
this guy is a god
1:22 algorithm - 1:29 recursion
1:54 go through the recursion tree
2:40
11:46 code
Thanks
Awesome !
Beautiful
Thank you for your explanation! It is pretty useful!
we don't need to create a new list every time.
We can use :-
def permute(self, nums: List[int]) -> List[List[int]]:
n = len(nums)
ans = []
def rec(path):
if not len(nums):
ans.append(path[:])
return
for i in nums[::-1]:
nums.remove(i)
path.append(i)
rec(path)
path.pop()
nums.append(i)
rec([])
return ans
heap’s algorithm is another way I believe.
Cracking the code interview book says the time complexity of this algorithm is not n!
The book is right, in the video he didn't take into consideration the additional time required to copy into a new array while removing the item.
nice explanation,thanks man.
Great explanation. Thanks!
Keep on teaching Man!
Very well explained video, thanks.
Best explanation thanks :)
Thank you.
dude amazing explanation thanks
best one
num.filter has O(N) time complexity - your final complexity is even worse than O(N!). Be careful using this at interview!
These things always make sense in hindsight once it's been explained, but do normal people actually come up with these crazy-ass solutions on their own?? Need to figure out what to take away from this conceptually so I actually learn something instead of just being able to do this exact problem if I see it again.
I guess this is how it works. Problem solving is all about reading the pattern, the more problems you solve the faster you can come up with an idea what algorithm you need to apply. You can say that we are memorizing it, but who isn't? That's why "Practice makes perfect" is a thing. I know I probably never gonna use it again when really working, unless I am on an algorithm research team, but this is how the game is played.
Exactly What I've been thinking.how on Earth do these guys come up with such solutions.😂😂😂
good explanation
😇
genius
I am really curios about what is really going on behind the scenes , for example when for the first time Permumation returns ,the next line set.pop is executed. But what was the next step? did it return back to the previous function call in the callStack( which seems it is) or did it just increased the i in for loop?
IN line 6 why are we doing this : answers.push([...set]) and why not simply this : answers.push(set)? set is an array and we can simply push the array into the answers array ?
Very nice explanation. Can someone show how to do write this in Python?
thanks m8
What is the space complexity for this
I had to play 0.75 speed. But so far its okay, will update after I complete video.
Anyone know the space complexity?
Thanks in advance!
I believe O(n!) as well because the array containing the answers will have that many elements
Its O(n!). The amount of permutations that you make is n!. your array then is n! large.
Hi. I've tried to make sense of this for a long time but I am still confused. Because the base case (the if statement) is checked for before the nums array is filtered, shouldn't there be one more step of the tree for just pushing the array into the answers. In the tree showed in the video, the last step does not meet the base case because nums is not empty when the base case is checked (nums === [3]). Could someone please clarify?
I used Java to write this code. But I get something wrong, the result would be [1,2,3] only. What's the problem with my code here? And how to write it correctly in Java?
public List permute(int[] nums) {
if (nums == null || nums.length == 0) return new ArrayList();
List result = new ArrayList();
List list = new ArrayList();
for (int num: nums) list.add(num);
List todo = new ArrayList();
backtrack(list, todo, result);
return result;
}
private void backtrack(List list, List todo, List result) {
if (list.size() == 0) {
result.add(new ArrayList(todo));
}
for (int i = 0; i < list.size(); i++) {
todo.add(list.get(i));
list.remove(i);
backtrack(list,todo,result);
todo.remove(todo.size() - 1);
}
}
Thanks a lot!
Would you mind explaining how this code will work in a stack frame?
Make more videos
is it allowed to add function parameters? not leaving just the "nums" one? I thought you are not allowed to do that....
Can somebody do this with java. ?
Good explanation but im not sure if its the best implementation in code? I've seen this problem solved using 2 functions with different # of parameters, what are the pros and cons of both implementations?
Why do we have to do set.pop()
Can any one please share code for Java ?
I used general backtracking logic to implement this. I don't know if its similar to what he is implementing as I only watched the logic.
class Solution {
List list=new ArrayList();
public List permute(int[] nums) {
backtrack(new ArrayList(),nums);
return list;
}
public void backtrack(List curr,int[] nums)
{
if(curr.size()==nums.length)
{
list.add(new ArrayList(curr));
}
for(int i=0;i
@@jagritbhupal5836 hey, can you please explain the code by chance?
Why poke yourself in the eyes and into the brain when learning a new concept whilst coding with Java will do it for you.
Can u please add java code?
Can someone give the java version of this?
Yes
you lost me 3 minutes in lol...