Shortest Distance to Target in Circular Array LeetCode 2515

🚀 Day 66 of #100DaysOfCode Today, I solved LeetCode 73 – Set Matrix Zeroes, a classic problem that tests in-place matrix manipulation and optimization techniques. 💡 Problem Overview: Given a matrix, if any cell contains 0, its entire row and column must be set to 0. The challenge is to perform this efficiently without using excessive extra space. 🧠 Approach: To solve this optimally, I focused on: ✔️ Using the first row and first column as markers ✔️ Tracking whether the first row/column initially contained zero ✔️ Updating the matrix in-place based on these markers This avoids using additional space and achieves optimal performance. ⚡ Key Takeaways: In-place algorithms help reduce space complexity Using matrix itself as storage is a powerful optimization trick Handling edge cases (first row/column) is critical 📊 Complexity Analysis: Time Complexity: O(n × m) Space Complexity: O(1) Improving problem-solving skills one day at a time 🚀 #LeetCode #100DaysOfCode #DSA #Matrix #ProblemSolving #CodingJourney #SoftwareDevelopment #InterviewPrep

🚀 Day 31 | 100 Days of Coding Challenge #DrGViswanathanChallenge 📘 Problem Solved: Implement Stack using Queues (LeetCode) 🔧 Approach Used (Queue Rotation): • Used a single queue to simulate stack behavior • On every push, inserted the element and rotated previous elements behind it • Ensured the last inserted element stays at the front (LIFO behavior) 📌 Key Idea: After pushing a new element, rotate the queue so that it behaves like a stack. ⏳ Complexity: Push: O(n) Pop: O(1) Top: O(1) Space: O(n) 🧠 Key Learning: Data structures can be implemented using others by cleverly adjusting operations—here, queue is used to mimic stack behavior. 📂 Topics Covered: Stack, Queue, Data Structures, Simulation #100DaysOfCode #DSA #CPP #LeetCode #CodingJourney #CompetitiveProgramming

🚀 Day 76 of #100DaysOfCode Today, I solved LeetCode 18 – 4Sum, a classic problem that extends the two-pointer technique to higher complexity. 💡 Problem Overview: Given an array, the task is to find all unique quadruplets that sum up to a target value. 🧠 Approach: ✔️ Sorted the array to simplify processing ✔️ Fixed two elements using nested loops ✔️ Applied the two-pointer technique for the remaining two elements ✔️ Carefully handled duplicates to ensure unique quadruplets This approach efficiently reduces unnecessary computations compared to brute force. ⚡ Key Takeaways: Sorting + Two Pointers is a powerful combination Avoiding duplicates is crucial in combination problems Breaking a complex problem into smaller parts simplifies logic 📊 Complexity Analysis: Time Complexity: O(n³) Space Complexity: O(1) (excluding output) Consistently pushing boundaries and solving more complex problems 🚀 #LeetCode #100DaysOfCode #DSA #TwoPointers #ProblemSolving #CodingJourney #SoftwareDevelopment #InterviewPrep

🥷 Day 343 of My 365 LeetCode Challenge is done! 💥 Kicked off strong, solved my problem, and the coding vibe is awesome! 🧠 🚀 Solved: Closest Equal Element Queries Today’s problem looked simple at first, but it had a clever twist involving circular arrays and efficient searching. 🔍 Problem Overview: You’re given an array and multiple queries. For each query index, the goal is to find the minimum distance to another index having the same value, considering the array as circular. If no such index exists, return -1. 💡 Key Observation: Brute force would be too slow ❌ Instead, the idea is to: Store positions of each value 📍 Use binary search to quickly find the nearest occurrence Handle the circular nature by checking both directions 🧠 Approach: Preprocess the array using a map (value → list of indices) For each query: Get the list of indices for that value Use binary search to locate the current index Check neighbors (left & right) to compute minimum distance Don’t forget to account for circular wrapping 🔄 ⚡ Why this works: Preprocessing reduces repeated work, and binary search ensures efficient lookups, making the solution scalable even for large inputs. 🔥 Takeaway: Sometimes optimization comes from organizing data smartly rather than complex logic. #LeetCode #DSA #BinarySearch #DataStructures #Coding #ProblemSolving #Cpp #TechJourney

Day 88 on LeetCode Add Two Numbers 🔗➕💡 A classic linked list problem focusing on digit-wise addition with carry handling. 🔹 Approach Used in My Solution • Traverse both linked lists simultaneously • Maintain a carry variable for overflow • Add corresponding node values + carry • Create new nodes for result using a dummy head • Continue until both lists and carry are fully processed ⚡ Complexity: • Time Complexity: O(max(n, m)) • Space Complexity: O(max(n, m)) 💡 Key Takeaways: • Dummy node simplifies linked list construction • Carry handling is the core concept in this problem • Reinforces simulation of real-world arithmetic using linked lists 🔥 Another step forward in strengthening pointer-based problem solving. #LeetCode #DSA #Algorithms #DataStructures #LinkedList #AddTwoNumbers #CarryLogic #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #Consistency #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

Spent some time solving 3 really interesting LeetCode contest problems today, and each one reinforced a different core DSA pattern. What I enjoyed most was how these seemingly small problems were actually testing pattern recognition more than implementation. 1) Mirror Frequency Distance This problem was all about symmetry and frequency mapping. Approach That I Used: Count the frequency of each character in the string. Map each character to its mirror counterpart: a ↔ z, b ↔ y 0 ↔ 9, 1 ↔ 8 Iterate through only half the range to avoid double counting. Sum the absolute frequency differences for every mirror pair. 2) Integers With Multiple Sum of Two Cubes This one immediately reminded me of the Ramanujan number (1729) concept. Approach That I Used: Generate all valid cube pairs (a, b) where: a³ + b³ ≤ n Store the frequency of each generated sum in a hashmap. Any number appearing 2+ times has multiple distinct cube representations. Return all such integers in sorted order. 3) Minimum Increments to Maximize Special Indices The most interesting one of the three because it involved DP state compression. & it took me an hour to do this question Approach: Treat every valid index as a candidate peak. Compute the cost required to make it greater than both neighbors. Maintain two rolling states: choose current index as peak skip current index Optimize first for: maximum number of peaks minimum cost among those choices Biggest learning from today: Contest questions often look implementation-heavy, but the real skill lies in identifying the hidden reusable pattern: frequency symmetry pair generation DP The more patterns we recognize, the faster we grow as problem solvers. Consistency is the real game changer. #LeetCode #DSA #DynamicProgramming #CompetitiveProgramming #ProblemSolving #C++ #CodingJourney #SoftwareEngineering

🚀 Day 91 of LeetCode Problem Solving Journey — 100 Days LeetCode Challenge Today, I solved LeetCode #209 — Minimum Size Subarray Sum using C++, under the guidance of Trainer NEKAL SINGH SALARIA Singh at REGex Software Services. 🔍 Problem Summary: Given an array of positive integers nums and a target value target, return the minimal length of a contiguous subarray whose sum is greater than or equal to target. If no such subarray exists, return 0. 🧠 Approach Used (Sliding Window): I used the sliding window technique to efficiently find the minimum length: Initialize two pointers → left = 0 and sum = 0 Expand the window by moving right and adding elements to sum While sum >= target: Update minimum length Shrink the window from the left by subtracting nums[left] Move left forward This approach dynamically adjusts the window to find the smallest valid subarray. 📚 Key Learnings of the Day ✔ Sliding window is powerful for subarray problems ✔ Expanding and shrinking window helps optimize search ✔ Works efficiently when all numbers are positive ✔ Avoids nested loops and reduces complexity ⏱ Complexity • Time Complexity: O(n) • Space Complexity: O(1) 💡 Optimization Insight: The brute-force approach takes O(n²), but sliding window reduces it to O(n) by maintaining a dynamic range. Getting closer to the finish line — see you on Day 92 🚀 #Day91 #100DaysLeetCodeChallenge #LeetCode #RegexSoftwareServices #NekalSingh #ProblemSolving #DSA #CPlusPlus #CodingChallenge #ProgrammingJourney #SlidingWindow #KeepGrowing

🚀 Day 84 of LeetCode Problem Solving Journey — 100 Days LeetCode Challenge Today, I solved LeetCode #73 — Set Matrix Zeroes using C++, under the guidance of Trainer NEKAL SINGH SALARIA Singh at REGex Software Services. 🔍 Problem Summary: Given an m x n matrix, if an element is 0, set its entire row and column to 0. The operation must be performed in-place. 🧠 Approach Used (Using Markers): I used the first row and first column as markers: Traverse the matrix and mark rows & columns that need to be zeroed Use matrix[0][j] and matrix[i][0] as flags Keep separate flags to track if the first row or first column should be zero Update the matrix based on these markers Finally update the first row and column if needed This avoids using extra space while keeping the solution efficient. 📚 Key Learnings of the Day ✔ Using matrix itself as storage avoids extra space ✔ Markers help track changes efficiently ✔ Careful handling of first row/column is important ✔ In-place modification requires planning ⏱ Complexity • Time Complexity: O(m × n) • Space Complexity: O(1) 💡 Optimization Insight: This is the optimal solution since it avoids extra arrays and uses constant space while modifying the matrix in-place. Consistency is becoming a habit — see you on Day 85 🚀 #Day84 #100DaysLeetCodeChallenge #LeetCode #RegexSoftwareServices #NekalSingh #ProblemSolving #DSA #CPlusPlus #CodingChallenge #ProgrammingJourney #Matrix #KeepGrowing

Day 23/128 of my LeetCode journey 🚀 Solved Contains Duplicate II — a great problem that helped reinforce the sliding window technique and efficient use of data structures. 💡 Key takeaway: Instead of checking all pairs, I used a sliding window with a Set to track elements within a range of size k. This avoids unnecessary comparisons and keeps the solution efficient. 🔍 What I practiced: Sliding window technique Efficient use of Set Optimizing time complexity Every problem might not be complex, but each one strengthens the fundamentals — and that’s what builds strong problem-solving skills over time. 💪 #LeetCode #DSA #CodingJourney #128DaysOfCode #SoftwareEngineering #ProblemSolving