Sliding Window Technique - Algorithmic Mental Models

[@omkarpat]

One of the best explanations of this concept. Please make some more "Algorithmic Mental Models" based videos.

[@CEOofTheHood]

dude u seriously need to make more of these. Ill pay for them.

[@andrewblaines]

Agreed! These are great. More "Algorithmic Mental Models" for dynamic programming, backtracking, etc. would be extremely helpful. Thanks for the video!

[@veliea5160]

it is not one of the best, it is the best

[@Mogwai88]

@@CEOofTheHood Yes. Please make more of these. Especially with your new freecodecamp traffic this would really take off.

[@AhmadRafique-yn8ry]

Agreed

[@jacktrainer4387]

I've never before seen a CS video that approached CS like math (i.e., here are the concepts, here are some keywords to look out for so you know when to apply these concepts, here are a few examples). The world needs 1,000 more videos like this (DP, Linked Lists, Trees, Graphs, etc). Fantastic work!

[@maxdrojjin6984]

This is really the best video (or any, really) explanation of the topic I have found. Really hope this would become more popular. Thank you!

[@divyanirao4279]

This video is amazing. hope you make more videos on "mental models" such as for dynamic programming.

[@michadobrzanski2194]

Max subarray sum of k is also a dynamic programming problem. You reuse the previous max result and update it. So it is like dynamic programming with space complexity O(1).

[@fahadhayat_]

Dude please create a playlist for the other techniques as well!!! This is gold!

[@zappist751]

Does he have a playlist for this?

[@maggiesayabie]

Who even has the audacity to dislike such a video?? The best and simplest explanation of sliding window concept i have ever come across. Thank you sir!

[@priyamrai5915]

will it work if array has negative elements. I think no.

[@sujityadawad]

Was struggling a lot with sliding window problems earlier, your explanation really simplified those problems. I really appreciate your effort. Thank you! Please post more videos.

[@4DChess]

Ryan where'd you go 😭😭 nobody else made vids as good as yours. I need you to cover the other topics too 🥺🥺🥺🥺

[@yynnooot]

Do you have a series for these Algorithmic Mental Models? Your video was extremely helpful and I would love to see more of these!

[@saugatkarki3169]

I looked into it and looks like this "Algorithmic mental models" video is the most viewed video on your channel. Upon reading the comments, people seems to really love it and are asking more of it covering other topics. Genuinely curious, why didn't you make more of this type of vide, that clearly seems to be the most viewed one on your channel?

[@shriduttkothari]

This is the only video which made me understand dynamic window sizing algorithm... Thank you so much 🥰

[@zackgrey4472]

For the smallest subarray code, wouldn't the time complexity be O(n^2) still because you added a while loop inside of your for loop? I recall the purpose of the sliding window technique is to convert quadratic time to linear, is this only for the fixed length and doesn't apply to dynamic? Or am I just misunderstanding the time complexity? Thanks for the wonderful explanation!!

[@potatocoder5090]

I have struggled with this concept for a really long time, but not anymore :) Your explanations were absolutely beautiful and I'm excited to solve some sliding window problems now! Can you please create more Algorithmic Mental Model videos? We'd all be super grateful!

[@diazdiaz3084]

Subscribed 😠😠 .. make more on algorithms ..

[@dr_920]

One of the best tutorials I have even seen. Thanks.

[@svdfxd]

One of the The best videos to explain Sliding windows concept. Request you to make other such videos that will help in tech interviews.

[@uzvalmallepeddi8962]

I wouldn’t resist to pay a 1000$ to watch such videos. Brilliant piece of work! Hope you do more videos on “Mental models”

[@silvahawk]

I have always been calculating the first windowSum in a separate loop before starting to "move" the window in a separate loop. I never knew you could do both within the same loop without using a nested loop. This is amazing!

[@neerajkulkarni6506]

This is fantastic! Please make more of these 'mental model' videos. There too many videos out there that jump straight to the solution without any discussion of how to approach and generalize a problem. We need more video's like this!

[@pjmiravalle]

I came across this video after struggling with sliding window problems in preparation for my upcoming Google interview, and just wanted to thank you for the super clear explanation. I'm feeling much more confident with these problems now. 👍

[@tomrobinson8290]

Did you get into google? How was the interview!?

[@giampierovanzzini1430]

This is a really great informative video, thanks for the hard work! I really like the breakdown to creating a mental model

Thanks, I flew through LeetCode Sliding Window challenges after watching this.

[@sedgeralt]

Hands down the best explanation of the Sliding Window technique. Please do more algorithmic mental model videos!

[@DonMamaril]

I loved, looooved the format of this! Please continue to do these videos. Extremely helpful!!

[@parthabhowmik4747]

The best lecture on sliding window technique

[@rohitsuresh3292]

LeetCode Links of similar problems to practice: 1. leetcode.com/problems/sliding-window-maximum/ 2. leetcode.com/problems/shortest-subarray-with-sum-at-least-k/ 3. leetcode.com/problems/longest-substring-with-at-most-k-distinct-characters/

[@VictorThomasCSC]

Holy shit great vid

[@amreshgiri]

🎯 Key points for quick navigation: 00:09 *🧠 Sliding Window Technique Overview* - Overview of the sliding window technique as an algorithmic mental model, - Benefits of using sliding window over naive approaches, - Introduction to fixed size and dynamically resizing sliding window techniques. 02:00 *🪟 Fixed Size Sliding Window Technique* - Explanation of fixed size sliding window using a rectangular metaphor, - Example of maximizing sum of a contiguous subarray using fixed size sliding window, - Comparison with brute-force approach highlighting efficiency gains. 07:00 *📊 Identifying Sliding Window Problems* - Characteristics of problems suited for sliding window technique, - Common keywords and problem types (min/max, longest/shortest, containment), - Applicability across arrays, strings, and linked lists. 13:38 *🎯 Types of Sliding Window Problems* - Fixed length variant: Example with fixed size subarrays and maximizing sums, - Dynamic resizing variant: Handling variable window sizes dynamically, - Dynamic variant with auxiliary data structures: Examples involving hash maps for advanced constraints like distinct characters. 17:05 *🔄 Commonalities in Sliding Window Problems* - Sequential nature of problems and contiguous data groupings, - Criteria for solving problems (maximization, minimization, containment), - Unifying approach to algorithmic problem solving using sliding window technique. 22:16 *📊 Sliding Window Technique for Fixed Window Size* - Explanation of the fixed-size sliding window technique, - Detailed walkthrough of how to maintain and update the sliding window, - Example problem solved using the fixed-size sliding window approach. 24:58 *🎯 Dynamic Sliding Window for Variable Window Size* - Introduction to dynamic sliding window where window size can vary, - Problem-solving approach to find the smallest subarray with a given sum, - Demonstration of adjusting window size dynamically based on problem constraints. 33:03 *🧩 Sliding Window with Auxiliary Data Structure* - Application of sliding window technique with an auxiliary data structure, - Problem-solving for finding the longest substring with K distinct characters, - Strategy for using hash maps to optimize tracking of character frequencies in the sliding window. Made with HARPA AI

[@subba18]

how do we get the actual subarray itself instead of length n these examples.

[@waseemmn2007]

OMG, Best problem solving explanation I've ever seen. it is a really helpful, simple, and to the point, Thank you for this extraordinary video. Liked and subscribed 🙌

[@shrey4489]

Great explanation! I'm a visual learner and I can see myself imagining these sliding window animations when I face array problems like this in the future.

[@vicente3j]

Only 219k views... no way. This is by far some of the best CS-related content on KZbin, hands down. Amazing!!

[@frozen_tortus]

I'm absolutelly blown away how this is explained.

[@yashj1072]

are you fucking kidding me? 7:16, I see that image and could immediately code it based on your previous graphical explanation.

[@Aestheticslay_yay]

//Java version of Longest substring with length k distinct characters public class DistinctWithKLetters { public static void main(String a[]){ Character[] charArray = {'A','A','A','H','H','B','B','B','B','B','I','I','I','C'}; System.out.println(findMaxLength(charArray, 2)); } private static int findMaxLength(Character[] charArray, int k) { Map map = new HashMap(); int start = 0; int maxValue = Integer.MIN_VALUE; int currentValue = 0; for(int i = 0; i < charArray.length ; i++) { map.put(charArray[i], map.getOrDefault(charArray[i], 0)+1); while(map.size() > k) { map.put(charArray[start], map.get(charArray[start])-1); if(map.get(charArray[start]) == 0){ map.remove(charArray[start]); } start++; currentValue = map.get(charArray[start]); //getting count of next character after deleted the first character. } maxValue = Math.max(++currentValue, maxValue); } return maxValue; }

[@mumarkhan1898]

In the second example 32:17 you're missing a condition in the inner while loop i.e windowStart should always be smaller or equal to windowEnd def get_smallest_sub_array_with_sum_greater_equal_to_a_value(a, k): left, right = 0, 0 min_window_size = float('+inf') current_sum = 0 while right < len(a): current_sum += a[right] while current_sum >= k and left

[@zhenliu4779]

really good. thank you But I think at 31:32, we should do if(currentWindowSum == targetSum){ minWindowSize = Math.min(minWindowSize, windowEnd-windowStart+1)}

[@go_lang_thang]

Hi Sir, I realyy respect you guy. Could you give me a list of Algorithmic Mental Models ? I wanna master DSA as soon as possible, bro !!!

[@drkenny7928]

for python lovers on the first question cur = 0 _max = cur t = [4,2,1,7] for i in range(len(t)): cur = sum(t[i:i+3]) if cur > _max: _max = cur else: cur = cur print(_max)

[@TheDannyDeez]

This video is pure gold, any chance you could make one on 2 pointers?

[@MsHofmannsJut]

There's a high-pitched tone at the beginning of your video. It's very, very unpleasant, I reflexively closed the tab. Please remove it.

[@vivienpmc]

Great explanation :) anybody here knows if there are there any tutorials with Javascript examples?. TIA!

[@sagaragrawal893]

Very nice video on sliding window concept. Can you make similar video for Dynamic programming and cover some of the examples.

[@shuvbhowmickbestin]

what a comprehensive video! Learnt a lot, liked and subbed. Keep bringing new content, you teach really good!

[@fisher00769]

I'm working on a Nonogram solver right now, but ran into some nasty time complexity issues with it, hopefully I can use this newfound knowledge to optimize it, really seems to be useful to that kind of problem.

[@rashidibrahim979]

for the last example, longest substring ... with k distinct. When hashmap.size > 2 is violated, why don't you just remove the starting characters all at once. For example, such as remove 'A' all at once. Instead of removing 'A' one by one. What is the point of decrementing the hashmap one by one ?

[@samarbajaj]

What the ... the way you explained it my 10. your old can understand it. Wowowo

[@TaraChand-eg6uk]

@Ryan : In case of smallestSubarray method, if targetSum is 9, it is failing, need to add another condition in it. private static int smallestSubarray(int targetSum, int[] input) { int currentWindowSum = 0; int minWindowSize = Integer.MAX_VALUE; int windowStart = 0; for (int windowEnd = 0; windowEnd < input.length; windowEnd++) { currentWindowSum += input[windowEnd]; while (currentWindowSum >= targetSum && windowEnd > windowStart ) { minWindowSize = Math.min(minWindowSize, windowEnd - windowStart + 1); currentWindowSum -= input[windowStart]; windowStart++; } } return minWindowSize; }

[@TheMikediaz100]

For the smallest subarray solution in 32:51, can you explain if the algorithm work for this test case? arr = [1,1,1,1,1,1,1,1] k =11

[@andresserron8596]

The guy did the job... I personally process this better, as I enjoy spatial representations. It seems that includes the Kadane's algorithm they struggle to explain, always falling back to the logic demonstration. If you cannot touch it, it doesn't exist. This I can touch.

[@alonbrim]

This video is pure gold!!! Great explanation . Everything is very clear. Thank you very much!

[@dayshiasweet]

This was a good video, but the continuous lip smacking made it hard to listen to.

[@akashbharatwaj6945]

Can you bring up a solution by having both negative and non negative numbers in the array??? For Example :find the Longest sub array with sum X for the given array {-5 8 -14 2 4 12}

[@SocajowaRS]

Please more videos like this, they are so good. Maybe some graph related videos?

[@plashless3406]

This is amazing. I wish I could hug you for this best explainantion. I learned a lot.

[@bananaboydan3642]

How is the naive brute force any different from the fixed sliding window solution for a max subarray of size k

[@alphapenguin9748]

Please make more algorithm videos, you're literally the best explainer I have found on youtube.

[@shashankshekhar5212]

Will it work if the input array has negative numbers [-1, -1, 1] and targetsum = 0

[@MrAlmas01kz]

One of the best and easy explanations of this concept. thank you so much !

[@syedaffanhameed14]

I found "Algorithmic mental models" is a great concept for solving problems please bring more videos like this.

[@SantoshPrajapati-ps9nj]

This is the best explaination that i have came across 🙂

[@benzybert5113]

amazing video! please add more mental algorithms (two pointer for example...)

[@s.bamahfoodh]

I don't understand where in the question it is asking for the subset to be sequential items. I mean why this subset is not the maximum [7, 8, 8]?

[@rainMeToSleep]

Hi @The Simple Engineer When are going to upload the videos on Trees and Graphs?

[@sahanatalanki6884]

This algorithm will fail if the array contains negative value. Can you post a video with a solution considering negative elements in the array?

[@TheDev05]

The animation for second technique: dynamic SW is just awesome, I loved it

[@Ash-fo4qs]

Graph, DP, Greedy, BFS-DFS, mental models plz.

[@msamogh96]

31:14 You'll have to add 1 even if the arrays weren't 0-indexed. Adding 1 has nothing to do with the indexing.

[@CEOofTheHood]

Please, If you are too busy to make videos will you please tell me a resource where I can learn these mental models. I am grinding leetcode and I get stuck on every new question. Please help.

[@vincenthou6459]

This is such an awesome video by abstracting the idea of sliding window to tackle a group of specific issues with similarities. Regarding the question of smallest subarray for a given target, I think we need to add the constraint that every element in the array is positive. Otherwise, the sum can be reduced even if we grow the size of the window without dropping the front one. 28:37

[@sierraobi311]

Please do! I want to recommend them to all my friends.

[@jm56481]

I don't comment much but this video is a triumph lol. Great job on the explanation Ryan.

[@emilmohaneriksson]

Perhaps an extra condition windowstart

[@glaze4629]

Great video. I wonder which intellij theme is thath? It's very pretty

[@alxx736]

Amazing, can you tell me the software for drawing?

[@amangautam2792]

Thanks for such nice explanation!

[@MrPrav6]

the dynamic windows example code fails for below test case: int[] nums = new int[] { 9, 4, 2,2}; //Act int actual = minSubarray.GetSmallestSubarray(nums, 8); int expected = 3; it will return 1.

[@victorklimov3175]

Thank you for GREAT explanation!

[@ripperx444]

Great video but Java blows ... Python is way easier to learn this

[@cocoarecords]

wouldnt mind buying a algorthmic mental models course from you tbh man

[@leonardo_magallanes]

Thank you so much for this video, I helped me a lot to understand this concept better.

[@darshantawte7435]

Pls make such videos on other important algorithmic models

[@sandeepk9640]

Thank You.. for demystifying 😍

[@andrewgraham3119]

Great explanation. It really helped me to fully understand

[@Mop_Deep]

It's been three years since you dropped this video. I wish you would cover the other patterns.

[@TheSimpleEngineer]

What patterns are you interested in?

[@Mop_Deep]

@@TheSimpleEngineer Like the ones in "Grokking the Coding Interview: Patterns for Coding Questions". You explained this to me so well. Would be dope if you did that for patterns from that list. i like your teaching style my friend. I would pay money if you had a course explaining those.

[@HologramJay]

@@TheSimpleEngineer Hi, Ryan. Hope you can clear something up for me. The solution for smallestSubArray isn't O(n^2) because you aren't looping over the array in the while loop, correct? You're just looping until the condition is no longer true. Is that right, or am I totally off?

[@klaaskabini5407]

Best explanation so far. Please make more videos on algorithmns

[@bestinbabu4244]

pls pls pls make more videos i beg you bro

[@jobhunter-j7d]

Longest substring length with K distinct chars int longestSubWithKDistinct(vector &A, int k){ int l = 0, r = 0; int ans = INT_MIN; unordered_map m; while (r < A.size()) { m[A[r]]++; while(m.size() > k){ m[A[l]]--; if(m[A[l]] == 0) m.erase(A[l]); l++; } ans = max(ans, r - l + 1); r++; } return ans; }

[@CantBeTamed53]

Great use of graphics to explain the algorithm! :D

[@leandrormor]

I want learn more algorithmic mental models :) thanks for shaing

[@vishwable]

Very beautiful Video...need more such videos.

[@isaidspaghetti]

Amazing video thank you friend!

[@monsterhoodlum]

Man i found you sooner. It would have been so much better.

[@mriduljain1981]

Doesnot it looks like english version of aditya verma xD

[@ethanwu7338]

Dude, thank you so much for making those videos!

[@downnloadz4483]

Amazing tutorial, thanks buddy

[@equaco]

Awesome!Please post up the code/links soon!

[@TheSimpleEngineer]

Added! Link in description

[@dolicious]

this was such a good explanation !! I hate Java but through your explanation I was able to understand what each line of code was doing. Even pointing out certain words that give away how one should solve the problem. Looking forward to future algo breakdowns !

[@alivation3409]

Same! I know this is an old comment, but I was able to mentally translate this into the programming languages I do know algorithms in. This is a great video.

[@abhiachalla4165]

Your variable names are enough to explain the code to a naive student

[@nirmalk55]

Damn goood... Best video on this subject !!