How to Solve LeetCode Problem #941: Valid Mountain Array

🚀 Day 47 of #100DaysOfCode – LeetCode Problem #1013: Partition Array Into Three Parts With Equal Sum 💬 Problem Summary: Given an array of integers, determine whether it can be split into three non-empty parts such that: Each part has the same sum, and The partitions appear in order (left → mid → right). Formally, we need to find indices i + 1 < j such that: sum(arr[0..i]) == sum(arr[i+1..j-1]) == sum(arr[j..end]) 🧩 Examples: Input: [0,2,1,-6,6,-7,9,1,2,0,1] Output: true ✔️ Three parts sum to 3 each. Input: [0,2,1,-6,6,7,9,-1,2,0,1] Output: false 🧠 Logic: This challenge focuses on finding equal prefix sums: 1️⃣ Compute total sum of the array. 2️⃣ If total sum is not divisible by 3 → ❌ impossible. 3️⃣ Walk through the array, accumulating values. 4️⃣ Each time a segment equals target = totalSum / 3, increase the partition count. 5️⃣ If we find 2 such partitions before the last index → array can be divided. 💻 Java Solution: class Solution { public boolean canThreePartsEqualSum(int[] arr) { int total = 0; for(int num : arr) total += num; if(total % 3 != 0) return false; int target = total / 3; int sum = 0, count = 0; for(int i = 0; i < arr.length; i++) { sum += arr[i]; if(sum == target) { count++; sum = 0; if(count == 2 && i < arr.length - 1) return true; } } return false; } } ⚙️ Complexity: Time: O(n) Space: O(1) ✅ Result: Accepted (Runtime: 0 ms) 💬 Takeaway: A great pattern-recognition problem—once you realize that only two valid partitions are needed (the third is implied), the solution becomes clean and efficient.

💻 Day 52 of #100DaysOfCodeChallenge Today’s problem: LeetCode 203 – Remove Linked List Elements 🧩 I worked on a Linked List manipulation problem where the goal is to remove all nodes that match a given value. This problem helped strengthen my understanding of pointer handling and dummy node usage in linked lists. 🧠 What I Learned: Handling edge cases where the head itself might need to be removed. Using a dummy node before the head to simplify pointer operations. How current and next pointers work together to modify a linked list safely. 🧩 Java Solution: class Solution {   public ListNode removeElements(ListNode head, int val) {     ListNode dummy = new ListNode(0);     dummy.next = head;     ListNode current = dummy;     while (current.next != null) {       if (current.next.val == val)         current.next = current.next.next;       else         current = current.next;     }     return dummy.next;   } } ⏱ Complexity: Time: O(n) Space: O(1) Every day, I’m getting more confident with data structures and linked list traversal logic.  On to the next challenge 🚀 #Day52 #LeetCode #Java #100DaysOfCode #CodingChallenge #LinkedList #ProblemSolving

💻 Day 52 of #100DaysOfCodeChallenge Today’s problem: LeetCode 203 – Remove Linked List Elements 🧩 I worked on a Linked List manipulation problem where the goal is to remove all nodes that match a given value. This problem helped strengthen my understanding of pointer handling and dummy node usage in linked lists. 🧠 What I Learned: Handling edge cases where the head itself might need to be removed. Using a dummy node before the head to simplify pointer operations. How current and next pointers work together to modify a linked list safely. 🧩 Java Solution: class Solution {   public ListNode removeElements(ListNode head, int val) {     ListNode dummy = new ListNode(0);     dummy.next = head;     ListNode current = dummy;     while (current.next != null) {       if (current.next.val == val)         current.next = current.next.next;       else         current = current.next;     }     return dummy.next;   } } ⏱ Complexity: Time: O(n) Space: O(1) Every day, I’m getting more confident with data structures and linked list traversal logic.  On to the next challenge 🚀 #Day52 #LeetCode #Java #100DaysOfCode #CodingChallenge #LinkedList #ProblemSolving

Problem 23 : LeetCode LeetCode Problem(268) Solved: First Missing Positive (Optimal In-Place Solution) 🤯 Problem: Given an unsorted array of integers, find the smallest positive integer that is not present in the array. This must be solved in O(n) time and O(1) extra space. My Approach: I solved this challenging problem using the Cyclic Sort algorithm for an optimal in-place solution. In-Place Hashing: I iterated through the array, ensuring that every number x (where 1≤x≤n) is placed at its correct index, which is x−1. For example, the number 3 belongs at index 2. Swapping: I repeatedly swapped elements that were out of place until they met the condition or were outside the valid range [1,n]. This effectively uses the array itself as a hash map. Final Scan: After the sorting phase, a final linear scan of the array quickly identified the first index i where arr[i] is not equal to i+1. The missing positive number is then i+1. Key Achievements: Time Complexity: Achieved an O(n) time complexity with a runtime of 1 ms, beating 31.66% of other Java submissions. Space Complexity: Achieved O(1) extra space, utilizing the array in-place. Memory: The solution used 47.56 MB of memory, beating 5.17% of other submissions. Link : https://lnkd.in/dJShvfwn

Day 2 of my #50DaysOfCode challenge Today, I tackled a problem that focuses on careful string parsing and numeric conversions String to Integer (atoi).It’s a great exercise that deepens understanding of data types, character operations, and overflow handling in Java. Problem: String to Integer (atoi) Implement the myAtoi(string s) function, which converts a string to a 32-bit signed integer. The algorithm for myAtoi(string s) is as follows: Ignore any leading whitespace (" "),Determine the sign by checking if the next character is '-' or '+', assuming positivity if neither present. Read the integer by skipping leading zeros until a non-digit character is encountered or the end of the string is reached. If no digits were read, then the result is 0. Solution: O(N) The solution iterates through each character of the string once. It first skips leading spaces, then checks for a sign, and builds the number digit by digit. At each step, it performs an overflow check to ensure the value stays within the 32-bit signed integer range. Finally, it multiplies the result by the sign and returns it as an integer. This efficient linear-time approach ensures both accuracy and robustness for different input formats.

Day 27 of #100DaysOfCode – LeetCode Problem #496: Next Greater Element I 🧩 Problem: Given two arrays nums1 and nums2, where nums1 is a subset of nums2, find the next greater element for each element of nums1 in nums2. If no greater element exists, return -1. 💡 Approach: Use a stack to track the next greater element for all numbers in nums2. Store results in a map for O(1) lookup when iterating nums1. This gives an efficient O(n) solution. 💻 Java Code: class Solution { public int[] nextGreaterElement(int[] nums1, int[] nums2) { Map<Integer, Integer> mpp = new HashMap<>(); Stack<Integer> st = new Stack<>(); for (int num : nums2) { while (!st.isEmpty() && st.peek() < num) { mpp.put(st.pop(), num); } st.push(num); } while (!st.isEmpty()) mpp.put(st.pop(), -1); int[] ans = new int[nums1.length]; for (int i = 0; i < nums1.length; i++) ans[i] = mpp.get(nums1[i]); return ans; } } ⏱️ Complexity: Time: O(nums1.length + nums2.length) Space: O(nums2.length) ✅ Result: Accepted (Runtime: 0 ms) This problem highlights how monotonic stacks can efficiently solve next-greater-element scenarios without nested loops. 

𝗕𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗦𝗰𝗲𝗻𝗲𝘀 𝗼𝗳 𝗝𝗮𝘃𝗮 𝗔𝗿𝗿𝗮𝘆𝗟𝗶𝘀𝘁! Today, I implemented a 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗔𝗿𝗿𝗮𝘆 in Java — building the logic from scratch to understand how ArrayList actually works under the hood. ⚙️ This exercise gave me hands-on exposure to how resizing, shifting, and accessing elements are efficiently handled in dynamic data structures. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 👇 🔹 add(element) — to append new items dynamically 🔹 add(index, element) — to insert at a specific position 🔹 get(index) — to fetch values efficiently 🔹 set(index, element) — to replace existing elements 🔹 remove(index/element) — to delete items and shift elements 🔹 contains(element) — to check for existence 🔹 isEmpty() & clear() — to manage and reset the structure 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝗱𝗲 : https://lnkd.in/gXEfwXvf 📘 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Recreating ArrayList from scratch deepened my understanding of how memory reallocation, indexing, and dynamic resizing make it one of the most powerful data structures in Java’s Collection Framework. Learning the “how” behind the “what” truly builds stronger foundations. 💪 #Java #DataStructures #ArrayList #CodingJourney #LearningByDoing #SoftwareEngineering #DSA #100DaysOfCode

🚀 Day 31 of #100DaysOfCode – LeetCode Problem #704: Binary Search 🧩 Problem: Given a sorted array and a target value, find the index of the target using an algorithm with O(log n) time complexity. If the target doesn’t exist, return -1. 💡 Approach: This is a classic Binary Search problem. We use two pointers (i and j) to represent the current search range. Calculate the middle index mid. If nums[mid] equals the target → return mid. If target > nums[mid] → search the right half. Else → search the left half. Repeat until the range is empty. 💻 Java Code: class Solution { public int search(int[] nums, int target) { int i = 0, j = nums.length - 1; while (i <= j) { int mid_i = i + (j - i) / 2; int mid_val = nums[mid_i]; if (target < mid_val) { j = mid_i - 1; } else if (target > mid_val) { i = mid_i + 1; } else { return mid_i; } } return -1; } } ⏱️ Complexity: Time: O(log n) Space: O(1) ✅ Result: Accepted (Runtime: 0 ms) Binary Search — simple, efficient, and one of the most fundamental algorithms every developer must master.

🚀 Day 96 of #100DaysOfCode Today’s challenge was all about implementing an LRU (Least Recently Used) Cache in Java — a problem that truly tests your understanding of data structures and memory optimization. 💡 🧩 Problem Statement: Design a data structure that supports: get(key) → Returns value if key exists, else -1 put(key, value) → Inserts or updates value Both should work in O(1) time complexity. It should automatically remove the least recently used entry when the cache exceeds its capacity. ⚙️ What I Implemented: ✅ Used LinkedHashMap with access order enabled. ✅ Overrode removeEldestEntry() to maintain fixed capacity. ✅ Ensured get() and put() operations are constant time. ✅ Tested the cache with sample inputs to confirm LRU behavior. 📘 Sample Output: When I executed the program, I got: {3=30, 1=10, 4=40} This confirmed that the oldest (least recently used) entry was correctly removed when the cache was full. 🧠 Concepts Strengthened: Understanding LinkedHashMap internals Efficient memory management O(1) data retrieval and replacement logic Practical use of overriding and encapsulation 🔥 Each day of this challenge pushes me closer to mastering clean, efficient, and optimized Java code. This one made me appreciate how real-world caching systems like browsers and databases manage performance. 💬 Next Goal: To explore advanced multithreading problems and optimize concurrent data structures. ✨ Almost at the finish line — Day 96/100! Every line of code is another step toward growth. 💪 #100DaysOfCode #Java #DataStructures #CodingChallenge #ProblemSolving #LinkedHashMap #SoftwareEngineering #LearningJourney #DeveloperCommunity

🚀 Day 34 of #100DaysOfCode – LeetCode Problem #228: Summary Ranges 🧩 Problem: Given a sorted, unique integer array nums, return the smallest list of ranges that covers all numbers in the array exactly. Each range is represented as: "a->b" if the range covers more than one number. "a" if it covers a single number. 📘 Example: Input: nums = [0,1,2,4,5,7] Output: ["0->2", "4->5", "7"] 💡 Approach: We traverse the array while tracking the start of each range. Whenever we detect a break (i.e., the next number isn’t consecutive), we close the range and store it. 🔍 Steps: Initialize start = nums[0]. Traverse the array. If the next element isn’t consecutive, push "start->current" (or "start" if equal). Move to the next starting number and continue. 💻 Java Code: class Solution { public List<String> summaryRanges(int[] nums) { List<String> result = new ArrayList<>(); if (nums.length == 0) return result; int i = 0; while (i < nums.length) { int start = nums[i]; int j = i; while (j + 1 < nums.length && nums[j + 1] == nums[j] + 1) { j++; } if (start == nums[j]) { result.add(String.valueOf(start)); } else { result.add(start + "->" + nums[j]); } i = j + 1; } return result; } } ⏱️ Complexity: Time: O(n) Space: O(1) ✅ Result: Accepted (Runtime: 3 ms) It’s a simple yet elegant problem — a great reminder that clean logic often beats complex tricks