Ali ELJALAOUI on LinkedIn: #cprogramming #bitmanipulation #codingtips #linkedinlearning (2024)

Hello 👋 connections, it's day no. 26 ✅ of 100daysDsaChallenge#100daysofleetcode and here is the problem that I solved today😊Problem:Valid ParenthesesProblem link: https://bit.ly/34kxPaqIntution:Here we used a stack data structure to check if a given string of parentheses is valid or not. The basic idea is to iterate through the string, and every time an opening parenthesis is encountered, it is pushed onto the stack. When a closing parenthesis is encountered, the code checks if there is a corresponding opening parenthesis at the top of the stack. If there is, it means that the parentheses are properly nested, and the opening parenthesis is popped from the stack. If not, or if there is no opening parenthesis in the stack when a closing parenthesis is found, the string is considered invalid.Here's a step-by-step breakdown:Initialize an empty stack to keep track of opening parentheses.Iterate through each character in the input string.If the character is an opening parenthesis ('(', '{', '['), push it onto the stack.If the character is a closing parenthesis (')', '}', ']'), check if the stack is not empty. If it's not empty, pop the top element from the stack and check if it matches the corresponding opening parenthesis for the current closing parenthesis. If it matches, continue; otherwise, return false.If the stack is empty after processing all characters, return true; otherwise, return false.Algorithmic Approach:The algorithm uses a stack to keep track of the opening parentheses, ensuring that each closing parenthesis has a corresponding opening parenthesis. It efficiently handles different types of parentheses and returns true if the parentheses are valid; otherwise, it returns false.Time Complexity:The time complexity of this code is O(n), where n is the length of the input string. This is because it iterates through each character in the string exactly once, and each operation inside the loop takes constant time.Space Complexity:The space complexity is O(n), where n is the length of the input string. In the worst case, the stack can have all the opening parentheses from the string.

4

Different Types of pointers===============wild pointerNull pointervoid pointerdangling pointer1.wild pointer:pointer that is unintialized is known as a wild pointer . performing any deferencing operation orpointer arithmetic will result in undefined behaviour.#include<stdio.h>int main(){int a=10;int *ptr;//wild pointerprintf("%d\n",*ptr);//undefined behaviour or value}2.Null pointer==========pointer that points to NULL is known as a NULL pointer ..performing any dereferncing or pointer arithmetic results in segmentation fault.basically NULL is a macro whose value is 0 defined already ..where ever NULL is written at the preprocessing stage it will be replaced with 0.#include<stdio.h>int main(){int a=10;int *ptr=NULL;//int *ptr=0;//NULL pointerprintf("%d\n",*ptr);//segmentation fault}Dangling pointer=============pointer pointing to a freed memory location or an unused memory location is termed as a dangling pointer.performing any pointer dereferncing or pointer arithmetic results in the existing value or undefined behaviour#include<stdio.h>int* fun();intmain(){int *ptr=fun();//dangling pointerprintf("%d\n",*ptr);//undefined behaviour}int* fun(){int a=10;int *p=&a;return p;}void pointer=========void pointer is also known as a generic pointer that can point to any type of datatype.but whenever we are performing any operations on void pointer we need to do the type convertion or type casting.type casting is nothing but changing the data from one type to another type.syntax===void *pointer_name;#include<stdio.h>int main(){int a=10;void *vptr=&a;printf("%d\n",*(int *)vptr);char ch='m';vptr=&ch;printf("%c\n",*(char *)vptr);float f=9.8;vptr=&f;printf("%f\n",*(float *)vptr);double d=789.09;vptr=&d;printf("%lf\n",*(double *)vptr);}

13

2 Comments

Different Types of pointers===============wild pointerNull pointervoid pointerdangling pointer1.wild pointer:pointer that is unintialized is known as a wild pointer . performing any deferencing operation orpointer arithmetic will result in undefined behaviour.#include<stdio.h>int main(){int a=10;int *ptr;//wild pointerprintf("%d\n",*ptr);//undefined behaviour or value}2.Null pointer==========pointer that points to NULL is known as a NULL pointer ..performing any dereferncing or pointer arithmetic results in segmentation fault.basically NULL is a macro whose value is 0 defined already ..where ever NULL is written at the preprocessing stage it will be replaced with 0.#include<stdio.h>int main(){int a=10;int *ptr=NULL;//int *ptr=0;//NULL pointerprintf("%d\n",*ptr);//segmentation fault}Dangling pointer=============pointer pointing to a freed memory location or an unused memory location is termed as a dangling pointer.performing any pointer dereferncing or pointer arithmetic results in the existing value or undefined behaviour#include<stdio.h>int* fun();intmain(){int *ptr=fun();//dangling pointerprintf("%d\n",*ptr);//undefined behaviour}int* fun(){int a=10;int *p=&a;return p;}void pointer=========void pointer is also known as a generic pointer that can point to any type of datatype.but whenever we are performing any operations on void pointer we need to do the type convertion or type casting.type casting is nothing but changing the data from one type to another type.syntax===void *pointer_name;#include<stdio.h>int main(){int a=10;void *vptr=&a;printf("%d\n",*(int *)vptr);char ch='m';vptr=&ch;printf("%c\n",*(char *)vptr);float f=9.8;vptr=&f;printf("%f\n",*(float *)vptr);double d=789.09;vptr=&d;printf("%lf\n",*(double *)vptr);}

266

15 Comments

𝐁𝐢𝐭 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐃𝐒𝐀, is very important and sometimes people fears as well ( including me ) so here are essential tips and tricks to unravel the magic of bit manipulation, which will definately gonna help you in Data Structures and Algorithms. 💻📌 𝐂𝐡𝐞𝐜𝐤𝐢𝐧𝐠 𝐢𝐟 𝐍𝐮𝐦𝐛𝐞𝐫 𝐀 𝐢𝐬 𝐄𝐯𝐞𝐧/𝐎𝐝𝐝- If `A & 1` equals 0, then A is even.- If `A & 1` equals 1, then A is odd.📌 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐲 (𝐋𝐞𝐟𝐭 𝐒𝐡𝐢𝐟𝐭) 𝐀 𝐛𝐲 𝐩𝐨𝐰𝐞𝐫𝐬 𝐨𝐟 𝟐- A = `A << k`.📌𝐃𝐢𝐯𝐢𝐝𝐞 (𝐑𝐢𝐠𝐡𝐭 𝐒𝐡𝐢𝐟𝐭) 𝐀 𝐛𝐲 𝐩𝐨𝐰𝐞𝐫𝐬 𝐨𝐟 𝟐- A = `A >> k`.📌 𝐒𝐞𝐭𝐭𝐢𝐧𝐠 𝐚 𝐣'𝐭𝐡 𝐁𝐢𝐭- A = A | (1 << j).📌 𝐔𝐧𝐬𝐞𝐭𝐭𝐢𝐧𝐠 𝐚 𝐣'𝐭𝐡 𝐁𝐢𝐭- A = A & (~(1 << j)).📌 𝐓𝐨𝐠𝐠𝐥𝐢𝐧𝐠(𝐅𝐥𝐢𝐩) 𝐣'𝐭𝐡 𝐁𝐢𝐭- A = A ^ (1 << j).📌 𝐂𝐡𝐞𝐜𝐤𝐢𝐧𝐠 𝐢𝐟 𝐣-𝐭𝐡 𝐁𝐢𝐭 𝐢𝐬 𝐒𝐞𝐭 𝐢𝐧 𝐀- If `(A & (1 << j)) > 0`, then the j-th bit in A is set.📌 𝐂𝐡𝐞𝐜𝐤𝐢𝐧𝐠 𝐢𝐟 𝐣-𝐭𝐡 𝐁𝐢𝐭 𝐢𝐬 𝐔𝐧𝐬𝐞𝐭 𝐢𝐧 𝐀- If `(A & (1 << j)) == 0`, then the j-th bit in A is unset.📌 𝐈𝐧𝐯𝐞𝐫𝐭𝐢𝐧𝐠 𝐚𝐥𝐥 𝐁𝐢𝐭𝐬 𝐢𝐧 𝐀- (A = ~A).📌 𝐕𝐚𝐥𝐮𝐞 𝐨𝐟 𝐋𝐞𝐚𝐬𝐭 𝐒𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 𝐁𝐢𝐭 𝐭𝐡𝐚𝐭 𝐢𝐬 𝐎𝐧 ( 𝐟𝐢𝐫𝐬𝐭 𝐟𝐫𝐨𝐦 𝐫𝐢𝐠𝐡𝐭)- T = (A & (-A))📌 𝐕𝐚𝐥𝐮𝐞 𝐨𝐟 𝐋𝐞𝐚𝐬𝐭 𝐒𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 𝐁𝐢𝐭 𝐭𝐡𝐚𝐭 𝐢𝐬 𝐎𝐟𝐟 ( 𝐟𝐢𝐫𝐬𝐭 𝐟𝐫𝐨𝐦 𝐫𝐢𝐠𝐡𝐭)- T = (~A & (A + 1)).📌𝐓𝐮𝐫𝐧 𝐎𝐧 𝐀𝐥𝐥 𝐁𝐢𝐭𝐬 𝐢𝐧 𝐚 𝐒𝐞𝐭 𝐨𝐟 𝐒𝐢𝐳𝐞 𝐧- A = (1 << n) - 1📌 𝐈𝐭𝐞𝐫𝐚𝐭𝐞 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐀𝐥𝐥 𝐒𝐮𝐛𝐬𝐞𝐭𝐬 𝐨𝐟 𝐚 𝐒𝐞𝐭 𝐨𝐟 𝐒𝐢𝐳𝐞 𝐧- for ( x = 0; x < (1 << n); ++x )Share your interesting tricks for bit manipulation; I'd love to learn, and others will benefit too. Let's uncover the magic together! 💻✨Follow Chandan Agrawal for more 😊😊#meme #dsa #interview #coding #fun #weekend #leetcode #bit #lc #gfg #cp #maang #google #amazon

603

7 Comments

𝟮𝟱 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝘆𝗼𝘂 𝗺𝘂𝘀𝘁 𝗻𝗼𝘁 𝗮𝘃𝗼𝗶𝗱1. 𝙎𝙤𝙧𝙩𝙞𝙣𝙜 𝘼𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢𝙨 🪄:🔹 Explore various techniques to arrange data in specific orders, including Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, and QuickSort2. 𝙎𝙚𝙖𝙧𝙘𝙝𝙞𝙣𝙜 𝘼𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢𝙨 🔍: 🔹 Master methods to efficiently locate specific elements within data collections, such as Linear Search and Binary Search.3. 𝙇𝙞𝙣𝙠𝙚𝙙 𝙇𝙞𝙨𝙩𝙨⛓️:🔹 Build a foundation in non-contiguous memory allocation by understanding linked list structures with operations like insertion, deletion, and reversal within linked lists.4. 𝙎𝙩𝙖𝙘𝙠𝙨 𝙖𝙣𝙙 𝙌𝙪𝙚𝙪𝙚𝙨🛢️:🔹 Master stacks (pancake towers) and queues (orderly lines) built with arrays or linked lists ✨5. 𝙏𝙧𝙚𝙚𝙨 🌴:🔹 Explore hierarchical data structures with a focus on binary trees.Know about traversal techniques (inorder, preorder, postorder) to navigate and process tree data.6. 𝙂𝙧𝙖𝙥𝙝𝙨 📊:🔹 Represent connections and relationships between entities using graphs. Implement graph traversal algorithms like Depth-First Search (DFS) and Breadth-First Search (BFS) for various applications.7. 𝙍𝙚𝙘𝙪𝙧𝙨𝙞𝙤𝙣 🔄:🔹 Understand the concept of self-referential functions and apply it to problems like factorial calculation and Fibonacci series generation.8. 𝘿𝙮𝙣𝙖𝙢𝙞𝙘 𝙋𝙧𝙤𝙜𝙧𝙖𝙢𝙢𝙞𝙣𝙜 🔢:🔹 Break down complex problems into overlapping subproblems for efficient solutions. Explore examples like Fibonacci calculation using dynamic programming and the 0/1 Knapsack problem.9. 𝙃𝙖𝙨𝙝𝙞𝙣𝙜 🔒:🔹 Implement hash tables for efficient storage and retrieval of data using key-value pairs. Practice hash table operations, including insertion, deletion, and search.10. 𝙃𝙚𝙖𝙥𝙨 ⛰️:🔹 Build a specialized tree-based data structure that maintains the heap property (parent nodes always greater/smaller than children). Learn Heapify and Heap Sort algorithms for efficient sorting.11. 𝘽𝙞𝙩 𝙈𝙖𝙣𝙞𝙥𝙪𝙡𝙖𝙩𝙞𝙤𝙣 ⚙️:🔹 Manipulate individual bits within data using bitwise operators (AND, OR, XOR, Shifts). Apply bit manipulation techniques for tasks like data compression and encryption.----------------------------------------------------------------FollowArijit Ghoshfor more insightful shares 📚1️⃣Telegram link to join ongoing activity and access many other coding resourceshttps://lnkd.in/dy2Hkh2m2️⃣ Like and share with your friends 🔄_______________________________________#dsa #algorithms #datastructuresandalgorithms #internship #hiring #graphs #sorting #stacks

68

11 Comments

Here's an interesting, useful and novel update to our brute-force variable subset selection (BFVSS) technique!Recently, we showed how a simple brute-force approach to perform parallelized and sparse data regression (PSDREG) via IMPL-DATA(c)'s variable subset selection was effective at finding multiple linear regression (MLR) models to the Dow Data Challenge Problem (DDCP) better than previously reported in the following papers:1. Braun et. al., IFAC, (2020),2. Qin et. al., CACE, (2021) and3. Barton & Lennox, Dig. Chem. Eng., (2022).As previously mentioned, the DDCP is a 44 x 1 MISO problem and supplies both the raw training and testing data.The past studies above employed several different and well-known machine learning methods where the best Q2 found was 0.698 (i.e., R2 for the testing data). In our previous two posts, we found a Q2 = 0.716 with eight (8) regressors using a mathematical programming VSS approach and Q2 = 0.718 with five (5) predictors using our BFVSS with N = 1000 number of shuffles, selections, samples, scenarios or situations varying the number of explanatory variables from 1 to 12 in parallel with the same random seed.However, here we apply something different using IMPL-DATA's semantic of "coefficient-setups" which includes or excludes an independent variable's regression coefficient i.e., zero (0) to exclude and one (1) to include. In our last post, we highlighted the Green (1977) algorithm to perform a "k" out of "m" shuffle or select which exogenously determines the coefficient-setups.Each coefficient-setup sample (n = 1..N) corresponds to a single MLR run. Below we detail two sets of Q2 results when N = 1000 and k = 2..13 (M = 44) where all runs use the same random seed.k= 2: Q2=0.625 -> Q2=0.641k= 3: Q2=0.669 -> Q2=0.693k= 4: Q2=0.687 -> Q2=0.711k= 5: Q2=0.682 -> Q2=0.723*k= 6: Q2=0.691 -> Q2=0.709k= 7: Q2=0.692 -> Q2=0.703k= 8: Q2=0.723 -> Q2=0.719*k= 9: Q2=0.701 -> Q2=0.723k=10: Q2=0.681 -> Q2=0.720k=11: Q2=0.699 -> Q2=0.705k=12: Q2=0.683 -> Q2=0.693k=13: Q2=0.689 -> Q2=0.695Now here's the difference, when we run the first set of Q2's, we fit or estimate a MLR by regressing the zero-one coefficient-setups w.r.t. to the mean squared error (MSE) of the testing data.By sorting their respective coefficients from negative to positive or smallest to largest, we arbitrarily record the top 22 (say 44 / 2 or any other subset) and only include the top subset when generating the coefficient-setups for the next set of samples for a particular k.As the results show for the second Q2's after "->" above, these Q2's are improved except for k = 8 and as the number of k's increase the Q2 values rise then fall where for k = 4..11, they are all better than the best Q2 = 0.698 found by others ;-)#industrial #algorithms #nonlinear #dynamic #optimization #control #industrialai #machinelearning

7

2 Comments

🔍 Harnessing Hashing: Unlocking Key-Value Pairs' Secrets🗝️Hash Tables: A Powerful Data StructureHash tables are a fundamental data structure that is used in a wide variety of applications. They are a type of associative array that maps keys to values. Hash tables are efficient for searching, inserting, and deleting elements.🎯What are Hash Tables?A hash table is a data structure that uses a hash function to map keys to values. The hash function takes a key and produces a hash value, which is an integer. The hash value is used to determine the location of the key in the hash table.♻ How are Hash Tables Used?Hash tables are used in a wide variety of applications, including:Symbol tables:Hash tables can be used to implement symbol tables,which are data structures that map names to values.🗳 Caches:Hash tables can be used to implement caches,which are data structures that store frequently accessed data.Databases:Hash tables can be used to implement databases,which are data structures that store data in a structured format.🔀Collision Resolution: Collisions occur when two different keys have the same hash value. There are a number of different collision resolution techniques that can be used to handle collisions. Some of the most common techniques include:▶ Separate chaining:Separate chaining uses a linked list to store the values that have the same hash value.▶ Linear probing:Linear probing probes the next available slot in the hash table until an empty slot is found.▶ Quadratic probing:Quadratic probing probes the hash table using a quadratic formula.📈Average Time Complexity:The average time complexity for searching, inserting, and deleting elements in a hash table is O(1). This means that the time it takes to perform these operations is independent of the size of the hash table.ConclusionHash tables are a powerful and versatile data structure that is used in a wide variety of applications. They are efficient for searching, inserting, and deleting elements. If you are not already familiar with hash tables, I encourage you to learn more about them.for more details please check out the: link: https://lnkd.in/dCZut4vY#hashtables #algorithms

2

🔍 Exploring the Basics: 𝐌𝐞𝐫𝐠𝐞 𝐒𝐨𝐫𝐭 🔍Hello, LinkedIn friends!👋 Let's have a look at a reliable and efficient sorting algorithm : Selection Sort!🗣️𝐌𝐞𝐫𝐠𝐞 𝐒𝐨𝐫𝐭 is a popular and efficient sorting algorithm that follows the divide and conquer strategy. It divides the unsorted list into n sub-lists, each containing one element, and then repeatedly merges these sub-lists to produce new sorted sub-lists until there is only one sub-list remaining, which is the sorted list.🌐💻Without going into coding, here's a quick look:🛠️ 𝐇𝐨𝐰 𝐢𝐭 𝐖𝐨𝐫𝐤𝐬:-> Divide : Divide the unsorted list into sub-lists, each containing one element.-> Conquer : Repeatedly merge sub-lists to produce new sorted sub-lists.-> Combine : Continue merging sub-lists until there is only one sub-list remaining, which is the sorted list.🎯 𝐖𝐡𝐲 𝐌𝐞𝐫𝐠𝐞 𝐒𝐨𝐫𝐭?-> Merge Sort is a reliable choice for general purpose sorting and a strong algorithm with a variety of uses. Merge Sort can help you with sorting large datasets, external sorting problems, and consistent performance all around. 🚀⏰ 𝐓𝐢𝐦𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲:-> Best case : O(nlog n)-> Average case : O(nlog n)-> Worst case : O(nlog n)🏆 𝐏𝐫𝐨𝐬:-> 🌐𝘚𝘵𝘢𝘣𝘭𝘦 𝘚𝘰𝘳𝘵𝘪𝘯𝘨 : Maintains the relative order of equal elements in the sorted output.-> ⏳𝘗𝘳𝘦𝘥𝘪𝘤𝘵𝘢𝘣𝘭𝘦 𝘗𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦 : It has a consistent and predictable performance of O(n log n) in the worst, average, and best cases, making it efficient for large data sets.-> 💪𝘌𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘚𝘰𝘳𝘵𝘪𝘯𝘨 : Ideal for situations where data exceeds main memory capacity.-> 📀𝘗𝘢𝘳𝘢𝘭𝘭𝘦𝘭𝘪𝘻𝘢𝘵𝘪𝘰𝘯 : The divide and conquer nature of Merge Sort allows for easy parallelization, making it suitable for parallel processing and multi-core systems.🚫 𝐂𝐨𝐧𝐬:-> 📈𝘚𝘱𝘢𝘤𝘦 𝘊𝘰𝘮𝘱𝘭𝘦𝘹𝘪𝘵𝘺 : The space complexity of Merge sort is O(n) because it requires additional space proportional to the size of the input array for the temporary storage of merged sub-lists. This can be a concern for large data sets with limited memory.-> 🔄𝘕𝘰𝘵 𝘈𝘥𝘢𝘱𝘵𝘪𝘷𝘦 : Even if the list is sorted, the merge sort goes through the entire process.-> 🗂️𝘕𝘰𝘵 𝘪𝘯 𝘗𝘭𝘢𝘤𝘦 : Merge sort is not in-place sorting algorithm, as it needs additional space for the merging process. This can be a drawback in situations where memory usage is a critical factor.-> 🐢𝘕𝘰𝘵 𝘱𝘳𝘦𝘧𝘦𝘳𝘢𝘣𝘭𝘦 𝘧𝘰𝘳 𝘴𝘮𝘢𝘭𝘭 𝘥𝘢𝘵𝘢𝘴𝘦𝘵 : While the time complexity is efficient at O(nlog n), the constant factors involved can make it slower for small datasets compared to simpler algorithms like Quick Sort and Insertion Sort. Follow Anshul Dohare for more such a knowledgeable content.#mergesort #dsa #datastructuresandalgorithms #algorithms #jobs #sorting #keepgrowing #computerscience #engineering #engineer #computersciencestudents #softwareengineer #programming #development #softwaredevelopment #learning #student #internships #success

11

🔍𝐉𝐮𝐬𝐭 𝐒𝐨𝐥𝐯𝐞𝐝 𝐚 𝐂𝐨𝐝𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞! 🔍 (With 4 Approaches)🧩 𝐏𝐫𝐨𝐛𝐥𝐞𝐦: "Find the Duplicate Number" (Medium)📚 𝐓𝐨𝐩𝐢𝐜𝐬: Algorithms, Arrays🏢 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬: Various📝 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧:You are given an array of integers nums containing n + 1 integers, where each integer is in the range [1, n] inclusive. There's only one repeated number in nums, and your task is to find and return this repeated number.Here's the catch: you must solve the problem without modifying the nums array, and you can only use constant extra space.𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝟏:- 𝐓𝐢𝐦𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: O(n log n)- 𝐒𝐩𝐚𝐜𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: O(1)This approach employs sorting to solve the problem. It sorts the given array of integers and then checks for consecutive duplicates. If found, it returns the duplicate number. While this solution works, it has a time complexity of O(n log n) due to sorting, which may not be optimal for larger input sizes.---𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝟐:- 𝐓𝐢𝐦𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: O(n)- 𝐒𝐩𝐚𝐜𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: O(n)Approach 2 uses a frequency array to count the occurrences of each number in the input array. It then scans this frequency array to identify the repeated number. This approach ensures a linear time complexity, but it uses additional space proportional to the size of the input array, making it less memory-efficient.---𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝟑:- 𝐓𝐢𝐦𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: O(n^2)- 𝐒𝐩𝐚𝐜𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: O(1)Approach 3 employs a nested loop to compare each pair of elements in the array, searching for duplicates. Unfortunately, this approach results in a time complexity of O(n^2), which can lead to a "Time Limit Exceeded" error for larger input sizes, making it less efficient than other solutions.---𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝟒:- 𝐓𝐢𝐦𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: O(n)- 𝐒𝐩𝐚𝐜𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: O(1)Approach 4 uses a clever algorithm known as Floyd's Tortoise and Hare (Cycle Detection) to find the duplicate number efficiently. It has a time complexity of O(n) and constant space complexity, making it the most optimal solution among the presented approaches.---These different approaches provide various trade-offs in terms of time and space complexity. Approach 4, with its linear time complexity and constant space usage, stands out as the recommended solution for efficiently solving this problem. However, it's valuable to explore and understand different approaches to problem-solving in order to enhance your coding skills.Approach1_sol: https://lnkd.in/dzV83SGAApproach2_sol: https://lnkd.in/dRFsuvMTApproach3_sol: https://lnkd.in/dKzX5R2pApproach4_sol: https://lnkd.in/dSAhimCx#CodingChallenge #LeetCode #Algorithms #Programming #ProblemSolving #ConstantSpace #FollowUpQuestions #array

15

🚀DSA Series - 𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝗟𝗶𝗻𝗸𝗲𝗱 𝗟𝗶𝘀𝘁 𝗠𝘆𝘀𝘁𝗲𝗿𝗶𝗲𝘀 𝘄𝗶𝘁𝗵 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀🚀Core set of algorithms and techniques to effectively tackle a broad range of linked list problems in technical interviews and software development. 𝟭. 𝗙𝗹𝗼𝘆𝗱’𝘀 𝗧𝗼𝗿𝘁𝗼𝗶𝘀𝗲 𝗮𝗻𝗱 𝗛𝗮𝗿𝗲 (𝗖𝘆𝗰𝗹𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻)𝗪𝗵𝘆 𝗜𝘁'𝘀 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹: Vital for identifying cycles in a linked list, determining the cycle's starting node, and assessing the cycle's length. Its applications go beyond cycle detection, including problems where detecting repetition or loops in data structures is required.𝟮. 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝗼𝗻𝗪𝗵𝘆 𝗜𝘁'𝘀 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹: Many linked list problems, such as reversing a list, can be elegantly solved using recursion. Recursion simplifies code for operations that involve backtracking or decomposing the problem into smaller, similar subproblems.𝟯. 𝗠𝗲𝗿𝗴𝗲 𝗦𝗼𝗿𝘁 𝗼𝗻 𝗟𝗶𝗻𝗸𝗲𝗱 𝗟𝗶𝘀𝘁𝘀𝗪𝗵𝘆 𝗜𝘁'𝘀 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹: Sorting is a common problem, and Merge Sort is particularly well-suited to linked lists due to its efficient divide-and-conquer approach, which does not require random access to elements. Understanding how to merge two sorted lists is also crucial for solving various problems that involve combining or rearranging data in sorted order.𝟰. 𝗧𝘄𝗼-𝗣𝗼𝗶𝗻𝘁𝗲𝗿 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀𝗪𝗵𝘆 𝗜𝘁'𝘀 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹: Beyond cycle detection, two-pointer techniques are invaluable for finding elements at a certain position from the end, detecting palindromes, and solving problems that require simultaneous traversal of a list at different rates or starting points.𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 𝗮𝗻𝗱 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀:-𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗮 𝗟𝗶𝗻𝗸𝗲𝗱 𝗟𝗶𝘀𝘁: Both iterative and recursive solutions.-𝗙𝗮𝘀𝘁 𝗮𝗻𝗱 𝗦𝗹𝗼𝘄 𝗣𝗼𝗶𝗻𝘁𝗲𝗿 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲: For finding the middle element, detecting cycles, and more.-𝗗𝘂𝗺𝗺𝘆 𝗛𝗲𝗮𝗱 𝗡𝗼𝗱𝗲: Useful for simplifying operations on the head of the list, such as insertions or deletions.-𝗜𝗻𝘁𝗲𝗿𝗹𝗲𝗮𝘃𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝗽𝗹𝗶𝘁𝘁𝗶𝗻𝗴: For problems that require rearranging nodes or separating a list into components based on certain criteria.If you found this post helpful, show your support below please! 👇👍 Like 🔄 Share 💬 Comment👤 Follow Sujeet Kumar🔔 Stay tuned for more updates#softwareengineering #softwaredevelopment #dsa #interview

5