100 Days LeetCode Challenge: Climbing Stairs Problem

✅ Day 46 of 100 Days LeetCode Challenge Problem: 🔹 #91 – Decode Ways 🔗 https://lnkd.in/gpZQshBr Learning Journey: 🔹 Today’s problem focused on counting the number of ways to decode a numeric string into letters. 🔹 I used a Dynamic Programming approach with space optimization, tracking only the previous two states. 🔹 At each step, I checked both single-digit and two-digit decoding possibilities. 🔹 Careful handling of edge cases like leading zeros was essential to ensure valid decoding paths. Concepts Used: 🔹 Dynamic Programming 🔹 Space Optimization 🔹 String Parsing 🔹 State Transition Key Insight: 🔹 Many decoding problems depend on evaluating valid transitions from previous states. 🔹 Maintaining only necessary previous results reduces space complexity to O(1). 🔹 Edge cases involving zeros are critical in avoiding invalid combinations. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 26 of 100 Days LeetCode Challenge Problem: 🔹 #746 – Min Cost Climbing Stairs 🔗 https://lnkd.in/gzuqP_J3 Learning Journey: 🔹 Today’s problem focused on finding the minimum cost required to reach the top of a staircase. 🔹 I used Dynamic Programming to compute the minimum cost starting from each step and working backward. 🔹 At every step, the decision is to move one or two steps ahead, choosing the path with lower accumulated cost. 🔹 Storing intermediate results avoids redundant calculations and improves efficiency. Concepts Used: 🔹 Dynamic Programming 🔹 Bottom-Up DP 🔹 State Transition 🔹 Optimization Techniques Key Insight: 🔹 Problems involving minimum cost often benefit from a bottom-up approach. 🔹 Comparing future states helps determine the optimal current decision. 🔹 Dynamic Programming simplifies problems that would otherwise be exponential using recursion. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 36 of 100 Days LeetCode Challenge Problem: 🔹 #213 – House Robber II 🔗 https://lnkd.in/gmvFEbZe Learning Journey: 🔹 Today’s problem extended the classic House Robber problem by arranging houses in a circular layout. 🔹 The circular constraint means the first and last houses cannot both be robbed. 🔹 I solved this by breaking the problem into two linear cases: excluding the first house and excluding the last house. 🔹 A helper function applies the standard dynamic programming approach to maximize profit without adjacent selections. Concepts Used: 🔹 Dynamic Programming 🔹 Space Optimization 🔹 Problem Decomposition 🔹 Greedy Decision Making Key Insight: 🔹 Circular constraints can often be simplified by converting them into multiple linear scenarios. 🔹 Tracking only previous states reduces space complexity while maintaining optimal results. 🔹 Recognizing problem patterns helps reuse solutions from related problems. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 25 of 100 Days LeetCode Challenge Problem: 🔹 #198 – House Robber 🔗 https://lnkd.in/gNEG2NE4 Learning Journey: 🔹 Today’s problem focused on maximizing the amount of money that can be robbed without alerting the police by robbing adjacent houses. 🔹 I solved it using Dynamic Programming by keeping track of two states: robbing the current house or skipping it. 🔹 At each step, the decision is based on the maximum profit from previous houses. 🔹 This approach avoids recursion and efficiently computes the result in a single pass. Concepts Used: 🔹 Dynamic Programming 🔹 State Transition 🔹 Iterative Optimization 🔹 Space Optimization Key Insight: 🔹 Problems involving optimal choices often reduce to tracking previous states. 🔹 Using only two variables is sufficient to represent the entire DP state. 🔹 This results in an efficient solution with linear time and constant space complexity. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 44 of 100 Days LeetCode Challenge Problem: 🔹 #3713 – Longest Balanced Substring 🔗 https://lnkd.in/gfryNgam Learning Journey: 🔹 Today’s problem focused on finding the longest substring where all characters appear with equal frequency. 🔹 I explored all possible substrings by fixing a starting index and expanding the window step by step. 🔹 A frequency array helped track character counts along with the number of distinct characters and maximum frequency. 🔹 By validating whether all active characters share the same count, I identified balanced substrings efficiently. Concepts Used: 🔹 String Processing 🔹 Frequency Counting 🔹 Nested Traversal 🔹 Sliding Window Concepts Key Insight: 🔹 Balanced substring problems rely on maintaining strict frequency conditions. 🔹 Tracking distinct characters and maximum frequency simplifies validation logic. 🔹 Smart bookkeeping can make brute-force approaches effective for constrained problems. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 47 of 100 Days LeetCode Challenge Problem: 🔹 #152 – Maximum Product Subarray 🔗 https://lnkd.in/gcKQWrHz Learning Journey: 🔹 Today’s problem focused on finding the maximum product of a contiguous subarray. 🔹 Unlike sum-based problems, negative numbers can flip the result, so I tracked both maximum and minimum products at each step. 🔹 Whenever a negative number appeared, I swapped the current max and min to maintain correctness. 🔹 This allowed me to dynamically update the best possible product while traversing the array once. Concepts Used: 🔹 Dynamic Programming 🔹 Kadane-like Optimization 🔹 Greedy State Tracking 🔹 Array Traversal Key Insight: 🔹 Keeping track of both maximum and minimum products is essential due to sign changes. 🔹 Negative numbers can turn the smallest product into the largest. 🔹 Maintaining rolling states leads to an efficient O(n) solution. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 31 of 100 Days LeetCode Challenge Problem: 🔹 #130 – Surrounded Regions 🔗 https://lnkd.in/gzziasbj Learning Journey: 🔹 Today’s problem focused on identifying and capturing regions in a 2D board that are fully surrounded. 🔹 I used graph traversal to explore connected regions of 'O' cells. 🔹 For each region, I tracked whether it touches the boundary of the board. 🔹 Only regions completely enclosed by 'X' were flipped, while boundary-connected regions were preserved. Concepts Used: 🔹 Breadth-First Search (BFS) 🔹 Graph Traversal 🔹 Matrix Traversal 🔹 Connected Components Key Insight: 🔹 Boundary-connected regions should never be captured. 🔹 Tracking region connectivity is essential to determine whether a region is surrounded. 🔹 Grid problems often reduce cleanly to graph traversal with careful boundary handling. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

✅ Day 43 of 100 Days LeetCode Challenge Problem: 🔹 #684 – Redundant Connection 🔗 https://lnkd.in/gWtKJ-FA Learning Journey: 🔹 Today’s problem focused on detecting an extra edge that creates a cycle in an undirected graph. 🔹 I used the Union-Find (Disjoint Set Union) data structure to track connected components. 🔹 For each edge, I checked whether both nodes already belong to the same set. 🔹 If they do, adding that edge forms a cycle, making it the redundant connection. Concepts Used: 🔹 Union-Find (Disjoint Set) 🔹 Path Compression 🔹 Graph Cycle Detection 🔹 Connected Components Key Insight: 🔹 Union-Find provides an efficient way to detect cycles during edge additions. 🔹 Path compression optimizes repeated parent lookups. 🔹 Incrementally building connectivity helps quickly identify redundant edges. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

Daily Coding Insight: Minimizing Maximum Pair Sum Just solved an interesting problem on LeetCode (#1877) that teaches a valuable pattern: Problem: Given an array of even length, form pairs to minimize the maximum pair sum. Key Insight: The optimal approach isn't intuitive at first! You might think of pairing similar numbers, but the actual solution is: Sort the array Pair smallest with largest (two-pointer approach) Why this works: By balancing each pair (small + large), we prevent any single pair from having an excessively large sum. Pattern Recognized: 🔹 Greedy + Sorting + Two Pointers 🔹 Time: O(n log n), Space: O(1) #Coding #Algorithm #ProblemSolving #Python #DataStructures #LeetCode #Programming #SoftwareEngineering #Tech #Learning

✅ Day 39 of 100 Days LeetCode Challenge Problem: 🔹 #114 – Flatten Binary Tree to Linked List 🔗 https://lnkd.in/g6xn2K3g Learning Journey: 🔹 Today’s problem focused on transforming a binary tree into a flattened linked list in-place. 🔹 I used an iterative approach, modifying pointers while traversing the tree. 🔹 For each node with a left subtree, I found the rightmost node of that subtree and connected it to the current node’s right subtree. 🔹 Then, I moved the left subtree to the right and continued traversal. Concepts Used: 🔹 Binary Trees 🔹 Tree Traversal 🔹 Pointer Manipulation 🔹 In-place Modification Key Insight: 🔹 Tree restructuring problems often rely on careful pointer adjustments. 🔹 Finding the predecessor (rightmost node of left subtree) helps preserve traversal order. 🔹 Iterative solutions can avoid recursion and reduce extra space usage. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity