🪐 Leetcode DS & Algo Resources

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Sliding Window Algorithm !! This is one of the popular algorithms because of its efficiency. But do you know, if you understand this algorithm/pattern, you will be able to solve many problems which are a slight variation of the standard algorithm? Thus, it's very important to learn algorithm/pattern and whenever you come across any new problem, try to recognise the pattern. Slight variations of Sliding Window pattern problems: https://lnkd.in/d4vNrUdu https://lnkd.in/dVg49PEc https://lnkd.in/d2YwauBU https://lnkd.in/dQjrAcJ5 https://lnkd.in/dHcY5mRe https://lnkd.in/dMrZYSDS https://lnkd.in/dEtTUpdx https://lnkd.in/dugnXyw6 https://lnkd.in/dr-ddg9c https://lnkd.in/dnCEwpHG https://lnkd.in/dVg49PEc https://lnkd.in/d32idnMc https://lnkd.in/dBi28ey8 https://lnkd.in/dGUFSsFx Basic template: 1. Expand the window 2. Meet the condition and process the window 3. Contract our window Some of the resources/blogs that you can check out for building thought process for sliding window algorithm: 1. https://lnkd.in/dVAFkZKw 2. https://lnkd.in/dd3me-mn 3. https://lnkd.in/dqvBsYag 4. https://lnkd.in/dBpBhDfD

Resources to improve Problem Solving on LeetCode Algorithms by Jeff Erickson - https://www.pdfdrive.com/algorithms-jeff-erickson-e91546043.html competitive programming 3 - https://www.pdfdrive.com/competitive-programming-3-d32649251.html Category list of each LC problem by wisdompeak - https://github.com/wisdompeak/LeetCode Rating list of each LC problem by zerotrac - https://https://lnkd.in/dXTz_tnY competitive programmers handbook - https://cses.fi/book/book.pdf

𝗦𝗼𝗺𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗼 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗶𝗻 𝗔𝗻𝘆 𝗖𝗼𝗱𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 1. Sliding Window 📌 The problem input is a linear data structure such as a linked list, array, or string 📌 You’re asked to find the longest/shortest substring, subarray, or the desired value 2. Two Pointers or Iterators 📌 It will feature problems where you deal with sorted arrays (or Linked Lists) and need to find a set of elements that fulfill certain constraints 📌 The set of elements in the array is a pair, a triplet, or even a subarray 3. Fast and Slow pointers 📌 The problem will deal with a loop in a linked list or array 📌 When you need to know the position of a certain element or the overall length of the linked list. 4. Merge Intervals 📌 If you’re asked to produce a list with only mutually exclusive intervals 📌 If you hear the term “overlapping intervals”. 📌 Intervals Intersection (medium) 📌 Maximum CPU Load (hard) 5. Cyclic sort 📌 They will be problems involving a sorted array with numbers in a given range 📌 If the problem asks you to find the missing/duplicate/smallest number in an sorted/rotated array 6. In-place reversal of linked list 📌 If you’re asked to reverse a linked list without using extra memory 7. Tree BFS 📌 If you’re asked to traverse a tree in a level-by-level fashion (or level order traversal) 8. Tree DFS 📌 If you’re asked to traverse a tree with in-order, preorder, or postorder DFS 📌 If the problem requires searching for something where the node is closer to a leaf 9. Two heaps 📌 Useful in situations like Priority Queue, Scheduling 📌 If the problem states that you need to find the smallest/largest/median elements of a set 📌 Sometimes, useful in problems featuring a binary tree data structure 10. Subsets 📌 Problems where you need to find the combinations or permutations of a given set 📌 Subsets With Duplicates (easy) 📌 String Permutations by changing case (medium)

Fast and Slow pointers The Fast and Slow pointer approach, also known as the Hare & Tortoise algorithm, is a pointer algorithm that uses two pointers which move through the array (or sequence/linked list) at different speeds. This approach is quite useful when dealing with cyclic linked lists or arrays. By moving at different speeds (say, in a cyclic linked list), the algorithm proves that the two pointers are bound to meet. The fast pointer should catch the slow pointer once both the pointers are in a cyclic loop. How To Identify - The problem will deal with a loop in a linked list or array - When you need to know the position of a certain element or the overall length of the linked list Questions - Linked List Cycle (easy) - Palindrome Linked List (medium) - Cycle in a Circular Array (hard)

Two Pointers Pattern Two Pointers is a pattern where two pointers iterate through the data structure in tandem until one or both of the pointers hit a certain condition. Two Pointers is often useful when searching pairs in a sorted array or linked list; for example, when you have to compare each element of an array to its other elements. How To Identify - It will feature problems where you deal with sorted arrays (or Linked Lists) and need to find a set of elements that fulfill certain constraints - The set of elements in the array is a pair, a triplet, or even a subarray Questions - Squaring a sorted array (easy) - Triplets that sum to zero (medium) - Comparing strings that contain backspaces (medium)

14 patterns to ace tech interviews 🔥 💎 Sliding Window 💎 2 Pointers or Iterators 💎 Fast and Slow Pointers or Iterators 💎 Merge Intervals 💎 Cyclic sort 💎 In-place reversal of linked list 💎 Tree BFS 💎 Tree DFS 💎 Two heaps 💎 Subsets 💎 Modified binary search 💎 Top K elements 💎 K-way merge 💎 Topological sort

𝐒𝐭𝐞𝐩-𝐛𝐲-𝐒𝐭𝐞𝐩 𝐠𝐮𝐢𝐝𝐞 𝐭𝐨 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐲𝐨𝐮𝐫 𝐃𝐒 𝐀𝐥𝐠𝐨 𝐬𝐤𝐢𝐥𝐥𝐬 📌 Focus on Quality not Quantity (Depth vs Breadth) 📖 Do not aim for solving tones of questions in a month, instead pick 100 quality questions, understand the logic, deduce patterns so that you can solve any question based on that technoque. 📌 Make a list of quality questions 📖 Research and make a list of quality questions in your excel sheet, start solving those and write those question which you were not able to solve. 📌 Master all the data structures with their implementations 📖 Already shared list on prev post, link in the comment section 📌 Repeat question 📖 Question you were not able to solve by your own, repeat them after few days, this is one of the underated step people dont follow. 📌 Make Note of Techniques and Patterns you observed in the solved questions 📖 Making fair notes for all the techniques algos and patterns will be very helpful, not only it will help you revise but isolating them will clear thepicture in your mind and you will be able to solve new questions based on that. 📌 Practice on NotePad/Paper 📖 Do not practice on IDE, Its you who want to learn and you are not testing the IDE's capability. 📌 Now Its time for covering Breadth 📖 Go on to any platform and start solving random problems as much as you can.

Roadmaps : https://youtu.be/hORRaq7CNw0 https://youtu.be/UVP3123wNYQ

Dynamic Programming Patterns !! Some standard Dynamic Programming solutions with different levels are given here. Do check below DP problems pattern-wise. Longest Increasing Subsequence variants: https://lnkd.in/gBM_sp78 https://lnkd.in/gX9Ahcq8 https://lnkd.in/gyqp7p-7 https://lnkd.in/gVncs-mU https://lnkd.in/g8PsjvUc https://lnkd.in/gPZjWxga https://lnkd.in/gFz-UzBy Partition Subset: https://lnkd.in/gHKJxMbN https://lnkd.in/gCYVAtdC BitMasking: https://lnkd.in/gNbrBE45 Longest Common Subsequence Variant: https://lnkd.in/g-gK5cUm https://lnkd.in/g8y-MF9K https://lnkd.in/gcKcgDsy https://lnkd.in/gTYPP8XX Palindrome: https://lnkd.in/gp62UKtj https://lnkd.in/g9eSKc7P Coin Change variant: https://lnkd.in/gVwSnnmW https://lnkd.in/gzsCcaxM https://lnkd.in/gbE2Ztrc https://lnkd.in/gnxGkJnw https://lnkd.in/g89B4Sx5 Matrix multiplication variant: https://lnkd.in/gfx6bgy9 https://lnkd.in/gSaPNNhQ https://lnkd.in/gknKY6gC Matrix/2D Array: https://lnkd.in/gyZgY4HP https://lnkd.in/gArgdbpu https://lnkd.in/gJ9U4WuJ https://lnkd.in/gkC8YzM5 https://lnkd.in/giyNEE7p https://lnkd.in/gyur-_Bj Hash + DP: https://lnkd.in/gWNd8faP https://lnkd.in/g3cZT64X https://lnkd.in/gCDX8kps https://lnkd.in/gctjnk6H State machine: https://lnkd.in/gTF3-c3E https://lnkd.in/gyhSA6Qt https://lnkd.in/g2ji_pEX https://lnkd.in/ggT5fBNr https://lnkd.in/g5avbqJH https://lnkd.in/gNpyHDXk Depth First Search + DP: https://lnkd.in/gmazHDpy https://lnkd.in/gnXBfmYU Minimax DP: https://lnkd.in/gzUPPYgX https://lnkd.in/gqFKRvSe

Sliding Window Pattern The Sliding Window pattern is used to perform a required operation on a specific window size of a given array or linked list, such as finding the longest subarray containing all 1s. Sliding Windows start from the 1st element and keep shifting right by one element and adjust the length of the window according to the problem that you are solving. In some cases, the window size remains constant and in other cases the sizes grows or shrinks. How To Identify - The problem input is a linear data structure such as a linked list, array, or string - You’re asked to find the longest/shortest substring, subarray, or a desired value Questions - Maximum sum subarray of size ‘K’ (easy) - Longest substring with ‘K’ distinct characters (medium) - String anagrams (hard)