Optimizing Intervals Between Identical Elements with Hashmap and Prefix Sum

Day 248 of #365DaysOfCode Solved a problem involving mirror pairs and index distance minimization using a hashmap-based approach. For each element, computed its digit-reversed counterpart and tracked indices to identify valid mirror pairs. The minimum distance was updated by comparing current positions with previously stored indices. The solution runs in O(n · d) time, where d is the number of digits, and efficiently leverages hashing for constant-time lookups. Continuing to build intuition around number manipulation and indexing strategies. #365DaysOfCode #Day248 #DSA #LeetCode #Python #Algorithms #HashMap #ProblemSolving #Consistency

Day 243 of #365DaysOfCode Solved Minimum Distance Between Three Equal Elements I using a hashmap-based indexing approach. Grouped indices of identical elements and evaluated triplets by scanning index lists to compute the minimum distance. This approach efficiently reduces redundant comparisons by leveraging value-based grouping. The solution runs in O(n) time with additional space for index storage. Continuing to refine problem solving through pattern recognition and efficient data organization. #365DaysOfCode #Day243 #DSA #LeetCode #Python #Algorithms #HashMap #ProblemSolving #Consistency

💡 Solved Group Anagrams today! 🧠 Key idea: Instead of comparing strings, I used a frequency array (size 26) to create a unique key for each word. ⚠️ Catch in constraints: All characters are lowercase English letters, which allows us to use a fixed-size array of 26 for efficient hashing. 🚀 Result: Achieved O(n · k) time complexity. Great example of converting a comparison problem into a hashing problem! #LeetCode #DSA #Algorithms #Python #CodingInterview

🚀 Day 48 – LeetCode Journey Today’s challenge: Substring with Concatenation of All Words ✔️ Solved using sliding window + hashmap ✔️ Processed string in fixed-length chunks ✔️ Tracked word frequency efficiently 💡 Key Insight: Instead of checking every substring blindly, splitting the string into word-sized pieces and using a hashmap helps validate matches efficiently. Sliding window keeps the solution optimized. This problem was a great exercise in string manipulation, hashing, and window techniques 🧠 Consistency is the real game changer 🚀 #LeetCode #Day48 #SlidingWindow #HashMap #Strings #Python #ProblemSolving #CodingJourney #100DaysOfCode https://lnkd.in/gxf4RBT6

✅ Day 26 of #DSAPrep > Problem: Merge Sorted Array > Platform: LeetCode > Concept: Two Pointers / Merge Technique Solved this problem by merging two sorted arrays using two pointers and storing the result in sorted order. > Key Idea: Use one pointer for each array Compare current elements Insert smaller value into result Add remaining elements at the end > Time Complexity: O(m + n) > Space Complexity: O(m + n) #DSAPrep #Algorithms #Python #TwoPointers #Arrays #ProblemSolving #CodingJourney

Group Anagrams: Frequency Signature Beats Sorting for Keying Sorting each string as key costs O(k log k) per string. Character frequency array is O(k) and creates same signature for anagrams. Fixed 26-element array converted to tuple serves as hashable HashMap key — cleaner, faster grouping. Frequency as Key: Character counts uniquely identify anagram groups. Tuple conversion enables using array as dict key (lists aren't hashable). This pattern applies to document clustering, duplicate detection. Time: O(n × k) vs O(n × k log k) with sorting | Space: O(n × k) #FrequencySignature #HashMap #Anagrams #KeyOptimization #Python #AlgorithmDesign #SoftwareEngineering

Last Stone Weight: Max-Heap via Negation for Collision Simulation Python's heapq is min-heap only. Simulate max-heap by negating values. Repeatedly extract two largest stones (most negative), compute difference, reinsert. Continue until one/zero stones remain. Max-Heap Workaround: Negating values transforms min-heap to max-heap. This pattern applies whenever max-heap needed in Python. Remember to negate back when extracting final values. Time: O(n log n) | Space: O(n) #Heap #MaxHeap #NegationTrick #PriorityQueue #Python #AlgorithmDesign #SoftwareEngineering

Kth Largest Element: Max-Heap via Negation for Selection Python's min-heap becomes max-heap by negating values. Build heap, extract k-1 largest elements, peek at top for kth largest. Alternative: maintain min-heap of size k for O(n log k) vs O(n log n) here. Better Approach: Min-heap of size k would be O(n log k) — push all elements, maintain heap size ≤ k. Top element is kth largest. This solution works but isn't optimal for large k. Time: O(n log n) | Space: O(n) #Heap #KthLargest #MaxHeap #NegationTrick #Python #AlgorithmDesign #SoftwareEngineering

🚀 Day 40 – LeetCode Journey Today’s problem: Linked List Cycle ✔️ Solved using Floyd’s Cycle Detection Algorithm (Tortoise & Hare) ✔️ Used two pointers (slow & fast) ✔️ Efficient cycle detection without extra space 💡 Key Insight: If a cycle exists, the fast pointer will eventually meet the slow pointer. This approach avoids using extra memory and works in O(n) time. This problem strengthened my understanding of linked lists and pointer techniques. 40 days of consistency — getting stronger every day 💪🔥 #LeetCode #Day40 #LinkedList #TwoPointers #Python #ProblemSolving #CodingJourney #100DaysOfCode

✅ Day 28 of #DSAPrep > Problem: Rearrange Array Elements by Sign > Platform: LeetCode > Concept: Array / Two Pointers Solved this problem using a two-pointer approach to place positive and negative numbers alternately in a new array. > Key Idea: Create a result array of same size Use one pointer for positive index (0,2,4...) Use another pointer for negative index (1,3,5...) Traverse input array and place elements accordingly > Time Complexity: O(n) > Space Complexity: O(n) #DSAPrep #Algorithms #Python #Arrays #TwoPointers #ProblemSolving #CodingJourney