Prefix Sum Pattern for Efficient Subarray Solutions

Day 2 of my #365DaysCodingChallenge | CodeOjas Journey Today, I solved 3 interesting array-based problems using Java: 🔹 Unique Subarray Sum Pairs – Found pairs of subarrays with equal sums using hashing techniques. 🔹 Array Rotation – Rotated array efficiently using optimized reversal approach. 🔹 Shifted Array – Implemented element shifting with proper handling of wrap-around using modular arithmetic. 💡 Approach: Focused on breaking problems into smaller parts, using concepts like subarrays, indexing, and optimization techniques. ⚡ Complexity: Most solutions were optimized to O(n) time with minimal space. 📌 Takeaway: Strong understanding of array manipulation and problem breakdown is key to solving DSA problems efficiently. Building consistency and improving every day 💪🔥 📂 Code: [Your GitHub Link] #DSA #Java #CodeOjas #365DaysOfCode #CodingJourney #ProblemSolving

Stop writing code that “just works.” Day 45 / 100 Days of DSA Problem: Find a pair from two arrays whose sum is closest to a target. First approach: Nested loops. Better approach: Two pointers (since arrays are sorted). Move intelligently based on the sum. Time Complexity: O(n + m). Same answer. Smarter thinking. Day 45 lesson: Don’t stop at “it works.” Push for optimal. #Day45 #100DaysOfCode #DSA #Java #ProblemSolving

Day 55 of #100DaysOfLeetCode 💻✅ Solved #434. Number of Segments in a String problem in Java. Approach: • Initialized a counter to track number of words (segments) • Traversed the string character by character • Checked for non-space characters • If the current character is not a space and either it is the first character or the previous character is a space, incremented the count • This ensures counting only the starting of each word Performance: ✓ Runtime: 0 ms (Beats 100% submissions) 🚀 ✓ Memory: 41.83 MB (Beats 99.81% submissions) Key Learning: ✓ Practiced string traversal without using extra space ✓ Learned how to identify word boundaries efficiently ✓ Improved logic building for handling spaces and edge cases Learning one problem every single day 🚀 #Java #LeetCode #DSA #Strings #ProblemSolving #CodingJourney #100DaysOfCode

Day: 53/365 📌 LeetCode POTD: Concatenation of Consecutive Binary Numbers Medium Key takeaways/Learnings from this problem: 1. This problem nicely mixes math + bit manipulation, especially understanding how many bits each number contributes. 2. Instead of literally concatenating strings, shifting the current result left by the bit-length is way more efficient. 3. It reinforces the idea that log2 helps you find bit length quickly for each number. 4. Big takeaway: always think in terms of binary operations when the problem screams “binary” — strings are usually a trap here. #POTD #365DaysOfCode #DSA #Java #ProblemSolving #LearningInPublic #Consistency 🥷

Day 60 of #100DaysOfLeetCode 💻✅ Solved #389. Find the Difference problem in Java. Approach: • Initialized a variable to store the sum • Subtracted ASCII values of all characters in string s • Added ASCII values of all characters in string t • The remaining value represents the extra character • Converted the result back to character and returned it Performance: ✓ Runtime: 3 ms (Beats 38.82% submissions) ✓ Memory: 43.34 MB (Beats 30.69% submissions) Key Learning: ✓ Learned a clever use of ASCII values for problem solving ✓ Practiced using math-based approach instead of extra data structures ✓ Improved understanding of string manipulation techniques Learning one problem every single day 🚀 #Java #LeetCode #DSA #Strings #Math #ProblemSolving #CodingJourney #100DaysOfCode

🚀 Day 1 of my DSA Journey with CodeOjas! Today, I solved a “Array Reversal in Blocks” problem using Java. Problem: Given an array of integers and a block size, reverse the array in blocks of the given size, but keep the middle block unchanged. Example: Input: arr = [1, 2, 3, 4, 5, 6, 7], blockSize = 3 Output: [3, 2, 1, 4, 5, 6, 7] Approach: Divided the array into blocks of the given size Reversed all blocks except the middle block Constructed a new array to store the modified result (brute-force approach) Complexity: Time Complexity: O(n) Space Complexity: O(n) This is a brute-force approach — simple, clear, and easy to understand for beginners. 💡 Takeaway: Breaking down a problem into blocks can help solve complex array manipulation problems easily. 📂 Code is available on my GitHub: [https://lnkd.in/g4yxAJs3] #DSA #Java #CodingJourney #CodeOjas #ProblemSolving #LinkedInLearning

Day 57 of #100DaysOfLeetCode 💻✅ Solved #506. Relative Ranks problem in Java. Approach: • Iterated through each player's score • Compared it with all other scores to determine its rank • Increased rank whenever a higher score was found • Assigned medals for top 3 ranks: Gold, Silver, Bronze • Converted remaining ranks to string and stored in result array Performance: ✓ Runtime: 124 ms (Beats 5.06% submissions) ✓ Memory: 46.76 MB (Beats 99.84% submissions) Key Learning: ✓ Understood ranking logic using comparison ✓ Practiced nested loop approach for problem solving ✓ Learned the importance of optimizing time complexity Learning one problem every single day 🚀 #Java #LeetCode #DSA #Arrays #Sorting #ProblemSolving #CodingJourney #100DaysOfCode

🚀 Day 97 of My 100 Days LeetCode Challenge | Java Today’s problem was a solid exercise in matrix processing and prefix sum optimization. The goal was to count the number of submatrices whose sum is less than or equal to a given value (k). A brute-force approach would be too slow, so the key was to use 2D prefix sums to efficiently compute submatrix sums. By converting the matrix into a prefix sum matrix, we can calculate the sum of any submatrix in constant time, making the overall solution much more efficient. ✅ Problem Solved: Count Submatrices With Sum ≤ K ✔️ All test cases passed (859/859) ⏱️ Runtime: 7 ms 🧠 Approach: 2D Prefix Sum 🧩 Key Learnings: ● Prefix sums are powerful for optimizing repeated range sum queries. ● 2D prefix sums extend the same idea from arrays to matrices. ● Preprocessing can drastically reduce computation time. ● Avoiding brute force is key in large input problems. ● Matrix problems often become easier with the right transformation. This problem reinforced how preprocessing techniques like prefix sums can turn complex problems into efficient solutions. 🔥 Day 97 complete — sharpening matrix optimization and prefix sum skills. #LeetCode #100DaysOfCode #Java #PrefixSum #Matrix #Algorithms #ProblemSolving #DSA #CodingJourney #Consistency

Day 49 of #100DaysOfLeetCode 💻✅ Solved #387. First Unique Character in a String problem in Java. Approach: • Created an array of size 26 to store the frequency of each character • Traversed the string and counted how many times each character appears • Traversed the string again to find first character with frequency equal to 1 • Returned the index of that character • If no unique character is found, returned -1 Performance: ✓ Runtime: 6 ms (Beats 84.58% submissions) ✓ Memory: 47.16 MB (Beats 36.29% submissions) Key Learning: ✓ Practiced using frequency arrays for string problems ✓ Learned how counting characters helps find unique elements efficiently ✓ Strengthened understanding of string traversal and indexing Learning one problem every single day 🚀 #Java #LeetCode #DSA #Strings #ProblemSolving #CodingJourney #100DaysOfCode

🚀 Day 27 / 180 – DSA with Java 🚀 📘 Topic Covered: Strings & Reverse Traversal 🧩 Problem Solved: Length of Last Word Problem: Given a string containing words separated by spaces, find the length of the last word. Approach: Started traversing the string from the end, skipped trailing spaces first, and then counted characters until encountering another space or reaching the beginning of the string. Key Learning: ✔️ Efficient reverse traversal in strings ✔️ Handling edge cases like trailing spaces ✔️ Writing simple yet optimized string logic If you’re also preparing for DSA, let’s connect and learn together 🤝 #DSA #Java #180DaysOfCode #LearningInPublic #Strings #ProblemSolving #Consistency