Floyd's Cycle Detection Algorithm Explained

🚀 Solved Today’s GeeksforGeeks POTD: Subarrays with First Element Minimum using Python 🐍 Problem: Given an integer array, count the number of subarrays where the first element is the minimum element of that subarray. Approach: Solved this problem using the Next Smaller Element technique with a stack: • For each element, found the next smaller element to its right • All subarrays starting at that index and ending before the next smaller element remain valid • Counted these ranges to compute the total number of valid subarrays efficiently This problem helped reinforce my understanding of: ✔️ Monotonic stack patterns ✔️ Next smaller element concept ✔️ Optimizing subarray problems from O(n²) to O(n) 💡 Time Complexity: O(n) 💡 Space Complexity: O(n) Consistent practice with DSA problems continues to improve my problem-solving skills and algorithmic thinking 💪 #geekstreak60 #npci #geeksforgeeks #dsa #python #algorithms #monotonicstack #codingjourney

✅ Day 18 of #DSAPrep > Problem: Insertion Sort > Concept: Sorting Algorithm Learned Insertion Sort, where elements are picked one by one and placed at the correct position in the sorted part of the array. > Key Idea: - Start from second element - Compare with previous elements - Shift elements to insert at correct position > Time Complexity: O(n²) > Space Complexity: O(1) #DSAPrep #Algorithms #Python #Sorting #ProblemSolving #CodingJourney

Column-Wise Comparison: Finding Longest Common Prefix in O(n×m) Instead of comparing strings pairwise, iterate character-by-character across all strings simultaneously. For each position, check if all strings have matching characters. First mismatch or string exhaustion? Return accumulated prefix. This column-wise approach processes each character exactly once. Why Column-Wise Wins: Early termination on first mismatch avoids processing remaining characters. Best case (common prefix is short): O(m) where m = prefix length. Worst case: O(n×k) where k = shortest string length. Time: O(n×m) where n = string count, m = min string length | Space: O(1) #StringAlgorithms #EarlyTermination #CommonPrefix #Python #AlgorithmOptimization #SoftwareEngineering

Search Insert Position: Binary Search with Left Pointer as Insertion Index Standard binary search returns -1 when not found. This variant returns insertion index instead. Key insight: when loop terminates without finding target, left pointer naturally points to where target should be inserted to maintain sorted order. Left Pointer Property: Binary search terminates when l > r. At this point, left has crossed past where target would fit. This makes left the correct insertion index without extra logic. Time: O(log n) | Space: O(1) #BinarySearch #InsertPosition #PointerProperties #SortedArrays #Python #AlgorithmDesign #SoftwareEngineering

💡 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 16 of #DSAPrep > Problem: Bubble Sort > Concept: Sorting Algorithm Implemented Bubble Sort by comparing adjacent elements and swapping them if they are in the wrong order. With each iteration, the largest element moves to its correct position at the end of the array. > Key Idea: - Compare adjacent elements - Swap if left element is greater than right - Repeat until the array is sorted > Time Complexity: O(n²) > Space Complexity: O(1) #DSAPrep #Algorithms #Python #Sorting #ProblemSolving #CodingJourney

Day 81 Heap patterns are starting to feel intuitive now. #Day81 🧩 973. K Closest Points to Origin Key idea: • Use a min heap • Calculate distance using squared values (no sqrt needed) • Push points into heap based on distance • Pop k closest elements What I liked about this problem: A small optimization (avoiding sqrt) makes it simpler and faster. These small tricks make a big difference. Consistency is turning patterns into instincts. #LeetCode #DSA #Python #Heap #PriorityQueue #LearningInPublic #Consistency

🔁 Rotate Matrix by 90° — Clean In-Place Trick! Solved the classic Rotate Matrix by 90° problem using a simple observation: • Transpose the matrix • Reverse it column-wise This approach rotates the matrix in-place with O(n²) time and O(1) extra space. Sometimes the best solutions come from simple transformations on the matrix. 🚀 #DSA #ProblemSolving #Python #CodingInterview #Algorithms

Limits are a core concept in calculus, and SymPy makes working with them surprisingly intuitive. Whether you're calculating the behavior of sequences, evaluating function limits, or deriving differential quotients, SymPy’s method gives you a powerful way to explore mathematical behavior symbolically. This post walks through practical examples of limits for sequences, functions, and difference quotients, complete with SymPy code you can run and adapt. #Python #SymPy #SymbolicMath #Calculus #RheinwerkComputingBlog #Limits Read here: https://hubs.la/Q048SdKL0

✅ Day 19 of #DSAPrep > Problem: Quick Sort > Concept: Divide and Conquer (Pivot & Partition) Learned Quick Sort, where an element is chosen as a pivot and the array is partitioned such that smaller elements are placed on the left and larger elements on the right. > Key Idea: - Choose a pivot element - Partition the array around the pivot - Recursively apply the same process on left and right subarrays > Time Complexity: O(n log n) > Space Complexity: O(log n) #DSAPrep #Algorithms #Python #Sorting #ProblemSolving #CodingJourney #DivideAndConquer