Several Coding Patterns for Solving Data Structures and Al.zip

# Several Coding Patterns for Solving Data Structures and Algorithms Problems during Interviews These are my notes in <b>Javascript</b> from a [course](https://www.educative.io/courses/grokking-the-coding-interview) that categorizes coding interview problems into a set of <b>16 patterns</b>. | | | |---|---| | <b>[Pattern 1: Sliding Window](./✅%20%20Pattern%2001%20:%20Sliding%20Window.md)</b>|<b>[Pattern 9: Two Heaps](./✅%20🙃%20Pattern%2009:%20Two%20Heaps.md)</b> | |<b>[Pattern 2: Two Pointer](./✅%20%20Pattern%2002:%20Two%20Pointers.md)</b>|<b>[Pattern 10: Subsets](./✅%20%20Pattern%2010:%20Subsets.md)</b>| |<b>[Pattern 3: Fast & Slow pointers](./✅%20%20Pattern%2003:%20Fast%20%26%20Slow%20pointers.md)</b>|<b>[Pattern 11: Modified Binary Search](./✅%20%20Pattern%2011:%20Modified%20Binary%20Search.md)</b>| |<b>[Pattern 4: Merge Intervals](./✅%20%20Pattern%2004%20:%20Merge%20Intervals.md)</b>|<b>[Pattern 12: Bitwise XOR](./✅%20Pattern%2012:%20%20Bitwise%20XOR.md)</b>| |<b>[Pattern 5: Cyclic Sort](./✅%20%20Pattern%2005:%20Cyclic%20Sort.md)</b>|<b>[Pattern 13: Top 'K' Elements](./✅%20Pattern%2013:%20Top%20'K'%20Elements.md)</b>| |<b>[Pattern 6: In-place Reversal of a LinkedList](./✅%20%20Pattern%2006:%20In-place%20Reversal%20of%20a%20LinkedList.md)</b>|<b>[Pattern 14: K-way merge](./%E2%9C%85%20Pattern%2014%3A%20K-way%20merge.md)</b>| |<b>[Pattern 7: Tree Breadth First Search](./✅%20%20Pattern%2007:%20Tree%20Breadth%20First%20Search.md)</b>|<b>[Pattern 15: 0/1 Knapsack (Dynamic Programming)](./%E2%9C%85%20Pattern%2015:%200-1%20Knapsack%20(Dynamic%20Programming).md)</b>| |<b>[Pattern 8: Depth First Search (DFS)](./✅%20%20Pattern%2008:Tree%20Depth%20First%20Search.md)</b>|<b>[Pattern 16: Topological Sort (Graph)](./%E2%9C%85%20Pattern%2016%3A%20%F0%9F%94%8E%20Topological%20Sort%20(Graph).md)</b>| ## Additional Resources Here are a few other resources that I found helpful when learning <b>Data Structures and Algorithms</b> using <b>JavaScript</b> - [Run JS](https://runjs.app/) - A JavaScript playground for your desktop - [Big O CheatSheet](https://www.bigocheatsheet.com/) - Reference for <b>Big-O</b> complexities of common algorithms - [Blind 75](https://www.teamblind.com/post/New-Year-Gift---Curated-List-of-Top-75-LeetCode-Questions-to-Save-Your-Time-OaM1orEU) - For additional practice, the [Blind75 list](https://www.teamblind.com/post/New-Year-Gift---Curated-List-of-Top-75-LeetCode-Questions-to-Save-Your-Time-OaM1orEU) of interview questions is 🔥. I would approach the questions in [this order](https://www.techinterviewhandbook.org/best-practice-questions) or [this order](https://neetcode.io/) or [create a custom Blind75 study plan](https://www.techinterviewhandbook.org/grind75) - [Edabit](https://edabit.com/) is a great resource if you need additional <b>Javascript</b> practice before you start using <b>leetcode</b>. - [LeetCode](https://leetcode.com/problemset/all/) of course 😋 - [Pramp](https://www.pramp.com/) - for mock interviews - [FreeCodeCamp's Algorithms](https://www.freecodecamp.org/learn/coding-interview-prep/#algorithms) - Extra practice, esp. the <b>Sort Algorithms</b> section. - [Build 15 JavaScript Projects - Vanilla JavaScript Course](https://www.youtube.com/watch?v=3PHXvlpOkf4) - for extra <b>Vanilla JavaScript/DOM Manipulation</b> practice. # #### 🌟 Challenge Questions #### 👩🏽‍🦯 Questions from <b>[Blind 75](https://www.teamblind.com/post/New-Year-Gift---Curated-List-of-Top-75-LeetCode-Questions-to-Save-Your-Time-OaM1orEU)</b> #### 😐📖 Questions tagged <b>FaceBook/Meta</b> #### 🌴 Questions tagged <b>Amazon</b> #### 🔎 Questions tagged <b>Google</b> ## [Pattern 1: Sliding Window](./✅%20%20Pattern%2001%20:%20Sliding%20Window.md) In many problems dealing with an array (or a <b>LinkedList</b>), we are asked to find or calculate something among all the contiguous subarrays (or sublists) of a given size. For example, take a look at this problem: > Given an array, find the average of all contiguous subarrays of size `K` in it. Lets understand this problem with a real input: `Array: [1, 3, 2, 6, -1, 4, 1, 8, 2], K=5` A <b>brute-force</b> algorithm will calculate the sum of every 5-element contiguous subarray of the given array and divide the sum by 5 to find the average. The efficient way to solve this problem would be to visualize each contiguous subarray as a sliding window of `5` elements. This means that we will slide the window by one element when we move on to the next subarray. To reuse the sum from the previous subarray, we will subtract the element going out of the window and add the element now being included in the sliding window. This will save us from going through the whole subarray to find the sum and, as a result, the algorithm complexity will reduce to `O(N)`. ## [Pattern 2: Two Pointer](./✅%20%20Pattern%2002:%20Two%20Pointers.md) In problems where we deal with sorted arrays (or <b>LinkedList</b>s) and need to find a set of elements that fulfill certain constraints, the [Two Pointers](./✅%20%20Pattern%2002:%20Two%20Pointers.md) approach becomes quite useful. The set of elements could be a pair, a triplet or even a subarray. For example, take a look at the following problem: > Given an array of sorted `numbers` and a `target` sum, find a pair in the array whose sum is equal to the given `target`. To solve this problem, we can consider each element one by one (pointed out by the first pointer) and iterate through the remaining elements (pointed out by the second pointer) to find a pair with the given sum. The time complexity of this algorithm will be `O(N^2)` where `n` is the number of elements in the input array. Given that the input array is sorted, an efficient way would be to start with one pointer in the beginning and another pointer at the end. At every step, we will see if the numbers pointed by the <b> two pointers</b> add up to the target sum. If they do not, we will do one of two things: 1. If the sum of the two numbers pointed by the <b> two pointers</b> is greater than the target sum, this means that we need a pair with a smaller sum. So, to try more pairs, we can decrement the end-pointer. 2. If the sum of the two numbers pointed by the <b> two pointers</b> is smaller than the target sum, this means that we need a pair with a larger sum. So, to try more pairs, we can increment the start-pointer. ## [Pattern 3: Fast & Slow pointers](./✅%20%20Pattern%2003:%20Fast%20%26%20Slow%20pointers.md) The <b>Fast & Slow</b> pointer approach, also known as the <b>Hare & Tortoise algorithm</b>, is a pointer algorithm that uses <b> two pointers</b> which move through the array (or sequence/<b>LinkedList</b>) at different speeds. This approach is quite useful when dealing with cyclic <b>LinkedList</b>s or arrays. By moving at different speeds (say, in a cyclic <b>LinkedList</b>), the algorithm proves that the <b> two pointers</b> are bound to meet. The <i>fast pointer</i> should catch the <i>slow pointer</i> once both the pointers are in a cyclic loop. One of the famous problems solved using this technique was [Finding a cycle in a <b>LinkedList</b>](https://github.com/Chanda-Abdul/Several-Coding-Patterns-for-Solving-Data-Structures-and-Algorithms-Problems-during-Interviews/blob/main/%E2%9C%85%20%20Pattern%2003:%20Fast%20%26%20Slow%20pointers.md#linkedlist-cycle-easy). Lets jump onto this problem to understand the <b>Fast & Slow</b> pattern. ## [Pattern 4: Merge Intervals](./✅%20%20Pattern%2004%20:%20Merge%20Intervals.md) This pattern describes an efficient technique to deal with overlapping intervals. In a lot of problems involving intervals, we either need to find overlapping intervals or merge intervals if they overlap. Given two intervals (`a` and `b`), there will be six distinct ways the two intervals can relate to each other: 1. `a` and `b`do not overlap 2. `a` and `b` overlap, `b` ends after `a` 3. `a` completely overlaps `b` 4.