Dynamic Programming & Pattern Observation in LeetCode Challenge

🚀 LeetCode Daily Challenge – Day 3 Problem #1411: Number of Ways to Paint N × 3 Grid (Hard) Today’s problem was a good example of how dynamic programming + pattern recognition can turn a complex-looking problem into an efficient solution. 💡 Approach & Thought Process: Instead of thinking about the entire grid at once, I focused on how one row affects the next row. For a single row of 3 cells: There are two valid coloring patterns: Type A: All three colors are different Type B: The first and third cells have the same color By counting how many ways each pattern can transition to the next row: We can track just two values instead of the full grid Each new row updates these values using fixed transition formulas This avoids brute force and keeps the solution efficient even for large n This reduces the problem to a simple iterative DP update. ⏱ Time Complexity: O(n) 📦 Space Complexity: O(1) This problem reinforced an important lesson: 👉 When constraints are large, look for repeating patterns instead of brute force. 📌 Stay tuned for more daily problem-solving insights and DSA practice updates! #LeetCode #DailyCoding #DataStructures #Algorithms #DynamicProgramming #ProblemSolving #Python #DSA #LearningInPublic #SoftwareEngineering

🗓 Day 64 / 100 – #100DaysOfLeetCode 📌 Problem 1339: Maximum Product of Splitted Binary Tree Today’s problem combined tree traversal with optimization logic. The task was to split a binary tree into two subtrees by removing exactly one edge, such that the product of the sums of the two resulting subtrees is maximized. 🧠 My Approach: First, computed the total sum of all nodes in the tree. Used a post-order DFS traversal to calculate the sum of each subtree. For every subtree: Considered it as one part after the split. The other part would have sum: total_sum − subtree_sum Calculated the product of these two values. Tracked the maximum product across all possible splits. Returned the result modulo 109+710^9 + 7109+7, as required. Post-order traversal works perfectly here because subtree sums must be known before evaluating the split. 💡 Key Learning: This problem reinforced: ✔ how post-order DFS is ideal for subtree-based calculations ✔ combining traversal with optimization criteria ✔ thinking globally (total sum) while evaluating local decisions (each split) A great example of how tree problems often require two passes: one for data collection and one for optimization. Another strong tree-based DP-style problem completed 🌳🚀 #100DaysOfLeetCode #LeetCodeChallenge #Python #ProblemSolving #BinaryTree #DFS #TreeDP #Algorithms #DSA #CompetitiveProgramming #SoftwareEngineering #CodingJourney #DeveloperJourney #LearningInPublic #TechStudent #CareerGrowth #Programming #KeepLearning

🗓 Day 65 / 100 – #100DaysOfLeetCode 📌 Problem 3799: Word Squares Today’s problem was an interesting mix of string indexing, permutations, and pattern validation. The task was to identify valid 4×4 word squares, where characters align correctly across rows and columns. 🧠 My Approach: Sorted the list of words for consistent ordering. Generated all possible permutations of 4 distinct words. Treated each permutation as: top, left, right, bottom Verified the word-square constraints by checking character alignment: First characters must match across corresponding positions. Last characters must align correctly to complete the square. If all conditions were satisfied, added the group of words to the result list. This direct validation approach works well given the fixed word length and limited input size. 💡 Key Learning: This problem reinforced: ✔ how careful index-based comparisons can enforce structural constraints ✔ using permutations to explore all valid combinations ✔ translating a visual pattern (word square) into precise logical checks It’s a good reminder that many pattern problems are about mapping geometry or structure into indices. Another satisfying problem solved 🚀 #100DaysOfLeetCode #LeetCodeChallenge #Python #ProblemSolving #Strings #Backtracking #Permutations #Algorithms #DSA #CompetitiveProgramming #SoftwareEngineering #CodingJourney #DeveloperJourney #LearningInPublic #TechStudent #CareerGrowth #Programming #KeepLearning

✅ Day 16 of 100 Days LeetCode Challenge Problem: 🔹 #79 – Word Search 🔗 https://lnkd.in/gHeRzA2d Learning Journey: 🔹 Today’s problem shifted from combination generation to grid-based search using DFS. 🔹 The goal was to check whether a given word exists in a 2D board by moving horizontally or vertically between adjacent cells. 🔹 I used Depth-First Search (DFS) with backtracking, starting from each cell that matched the first character of the word. 🔹 To avoid revisiting the same cell in a single path, I temporarily marked the cell as visited and restored it after exploring all directions. Concepts Used: 🔹 Depth-First Search (DFS) 🔹 Backtracking 🔹 Matrix Traversal 🔹 Recursive Search Key Insight: 🔹 Backtracking is not limited to combinatorial problems; it is equally effective for grid traversal and path search problems. 🔹 Marking and restoring visited cells is essential to correctly explore all possible paths. 🔹 Careful boundary checks and base conditions are critical for correctness in recursive grid problems. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 22 of 100 Days LeetCode Challenge Problem: 🔹 #17 – Letter Combinations of a Phone Number 🔗 https://lnkd.in/gmUqqEEf Learning Journey: 🔹 Today’s problem focused on generating all possible letter combinations from a digit string based on a phone keypad mapping. 🔹 I used a mapping of digits to characters and built combinations iteratively by merging results from previous digits. 🔹 Each step combines the existing set of strings with the characters of the next digit, gradually expanding the result space. 🔹 This approach avoids deep recursion and clearly demonstrates how combinatorial growth works step by step. Concepts Used: 🔹 Hash Maps 🔹 Iterative Combination Building 🔹 String Manipulation 🔹 Cartesian Product Logic Key Insight: 🔹 Phone keypad problems are classic examples of combinatorial explosion. 🔹 Breaking the problem into incremental combination steps makes it easier to reason about and implement. 🔹 Understanding how intermediate results grow is crucial for mastering combination-based problems. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

🗓 Day 66 / 100 – #100DaysOfLeetCode 📌 Problem 865: Smallest Subtree with All the Deepest Nodes Today’s problem focused on binary tree depth analysis. The goal was to find the smallest subtree that contains all the deepest nodes in the tree. 🧠 My Approach: Used Depth-First Search (DFS) to compute information bottom-up. For each node, tracked: the maximum depth reachable from that node, and the candidate subtree root that contains all deepest nodes. Compared depths of left and right subtrees: If both sides have the same depth → the current node is the answer. Otherwise, propagated the deeper side upward. Returned the node that satisfies the condition for all deepest nodes. This approach cleanly combines depth calculation with subtree selection in a single traversal. 💡 Key Learning: This problem reinforced: ✔ how returning multiple values from DFS simplifies logic ✔ thinking bottom-up for tree optimization problems ✔ identifying the exact node where depths converge Tree problems often become elegant once the right recursive structure is identified 🌳🚀 #100DaysOfLeetCode #LeetCodeChallenge #Python #ProblemSolving #BinaryTree #DFS #TreeTraversal #Algorithms #DSA #CompetitiveProgramming #SoftwareEngineering #CodingJourney #DeveloperJourney #LearningInPublic #TechStudent #CareerGrowth #Programming #KeepLearning

Solving the Bitwise OR Challenge 🔢 The problem: Find the smallest 'x' such that x OR (x + 1) equals a given prime number 'p'. The Logic: 💡 The only prime number that cannot satisfy this is 2. For all other primes (which are odd), the binary representation ends in at least one '1'. 💡 The operation x OR (x + 1) essentially fills the rightmost '0' bit of 'x' with a '1'. 💡 To minimize 'x' while satisfying the condition, we look for the rightmost sequence of consecutive 1s in the prime 'p'. 💡 By flipping the highest bit of that specific trailing sequence from 1 to 0, we create our answer. Example Walkthrough: 👉 For p = 13 (1101): The trailing 1 is at position 0. Flipping it gives 1100 (12). 12 OR 13 = 13. 👉 For p = 31 (11111): The sequence of 1s is long. Flipping the bit at the "boundary" gives 01111 (15). 15 OR 16 = 31. Code Implementation: https://htmlify.me/r/u0q7 Key Takeaway: When dealing with x OR (x + 1), you are looking at how a carry bit propagates through trailing ones in binary addition! #BitManipulation #Programming #Algorithms #Python #CompetitiveProgramming #TechTips

✅ Day 13 of 100 Days LeetCode Challenge Problem: 🔹 #90 – Subsets II 🔗 https://lnkd.in/gCyD9UNb Learning Journey: 🔹 Today’s problem was similar to yesterday’s (#78 – Subsets), but this time the array could contain duplicates, so we had to ensure the solution set did not contain duplicate subsets. 🔹 I approached it using backtracking, again making a binary decision at each index: include or exclude the current element. 🔹 To handle duplicates, I sorted the array first and skipped duplicate elements at the same recursion level, preventing repeated subsets. 🔹 Copying the current solution at each step preserves the subset before backtracking continues. Concepts Used: 🔹 Backtracking 🔹 Recursion 🔹 Depth-First Search (DFS) 🔹 Handling Duplicates Key Insight: 🔹 Sorting and carefully skipping duplicates is essential when generating unique subsets. 🔹 Subset problems map naturally to an include/exclude decision tree. 🔹 Backtracking remains a clean way to explore all combinations, even with duplicate elements. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

🚀 LeetCode + DSA — Day 20 Today I solved LeetCode 75: Sort Colors, a classic problem that strengthens in-place sorting and comparison-based logic. 🔹 Problem Overview You’re given an array containing only 0, 1, and 2, representing three different colors. The task is to sort the array in-place so that elements of the same color are adjacent and ordered as 0 → 1 → 2, without using the built-in sort function. 🔹 Approach Used ✔ Used a comparison-based in-place sorting strategy ✔ Iterated through the array and tracked the smallest element index ✔ Swapped elements to position them correctly ✔ Ensured no extra space was used 🔹 Why This Works In-place swapping avoids additional memory usage Controlled iteration guarantees correct ordering Simple logic, yet fully compliant with problem constraints 📊 Performance ✅ All test cases passed ⚡ Runtime: 0 ms (Beats 100%) 💾 Memory: Efficient in-place solution 💡 Key Takeaway Problems with strict constraints push you to think beyond library functions. Mastering in-place operations builds strong fundamentals for real-world systems where space efficiency matters. Consistent practice > complex solutions 💪 #LeetCode #DSA #Python #Arrays #Sorting #InPlaceAlgorithm #ProblemSolving #DailyCoding #CodingPractice #SoftwareDeveloperJourney #LearningByDoing

✅ Day 19 of 100 Days LeetCode Challenge Problem: 🔹 #208 – Implement Trie (Prefix Tree) 🔗 https://lnkd.in/gM9iinDt Learning Journey: 🔹 Today’s problem introduced a Trie (Prefix Tree), a data structure optimized for storing and searching strings efficiently. 🔹 I implemented insert, search, and prefix search operations. 🔹 Each Trie node contains a dictionary of children nodes and a flag to mark the end of a word. 🔹 This structure allows O(length of word) operations for insertion and lookup, making it extremely efficient for large word datasets. Concepts Used: 🔹 Trie / Prefix Tree 🔹 Hash Maps / Dictionaries 🔹 Recursion (conceptual for traversal) 🔹 String Manipulation Key Insight: 🔹 Tries excel at problems involving prefix queries or autocomplete. 🔹 Understanding the node structure and careful handling of the is_end_of_word flag is critical for correctness. 🔹 Tries provide a balance between memory usage and fast search performance compared to other data structures like hash sets for string collections. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity