Divide an Array Into Subarrays With Minimum Cost II | LeetCode 3013 | Sliding Window + TreeSet скачать в хорошем качестве

Divide an Array Into Subarrays With Minimum Cost II | LeetCode 3013 | Sliding Window + TreeSet

In this video, we solve LeetCode 3013: Divide an Array Into Subarrays With Minimum Cost II. Code: https://leetcode.com/problems/divide-... Upsolve Leetcode Contest:    • Leetcode Contests   Greedy & Heaps:    • Greedy & Heaps   Two pointers:    • Two pointers   Sliding Window:    • Sliding Window   Maths & Geometry:    • Maths & Geometry   Stack:    • Stack   Set & Map:    • Set & Map   Bit manipulation:    • Bit Manipulation   Backtracking:    • Backtracking   Linked List:    • Linked List   Binary Search:    • Плейлист   Graph:    • Graph   Dynamic Progamming:    • Dynamic Programming   🔹 Problem Summary: You are given an array nums, an integer k, and an integer dist. You need to divide nums into k disjoint contiguous subarrays. Cost = Sum of first elements of each subarray. Constraint: The starting index difference between 2nd subarray and kth subarray must be ≤ dist. Goal → Minimize total cost. ----------------------------------------------------- 🔹 Key Insight First subarray always starts from index 0 → cost fixed = nums[0] Now we must choose: k-1 starting indices from range [1 … dist+1] initially, Then slide window and maintain best (k-1) smallest elements. ----------------------------------------------------- 🔹 Optimal Approach Used Data Structures: • TreeSet → Maintain smallest (k-1) candidates • TreeSet → Maintain remaining candidates • Sliding Window → Maintain valid dist range Steps: 1. Fix nums[0] as first cost. 2. Maintain window of size dist. 3. Keep smallest (k-1) values using TreeSet. 4. Maintain running sum of selected values. 5. Slide window → update sets → update sum. 6. Track minimum answer. ----------------------------------------------------- 🔹 Why This Works Instead of recomputing smallest k each time: → Maintain dynamically using ordered structure. ----------------------------------------------------- 🔹 Complexity Time Complexity: O(n log dist) Space Complexity: O(dist) ----------------------------------------------------- 🔹 Concepts Covered ✔ Sliding Window ✔ Balanced BST (TreeSet) ✔ Greedy Selection ✔ Running Minimum Sum ✔ Window Maintenance Optimization ----------------------------------------------------- Great Hard problem combining Sliding Window + Ordered Set Optimization. #LeetCode3013 #DivideArrayMinimumCost #SlidingWindow #TreeSet #GreedyAlgorithm #HardLeetCode #LeetCodeDaily #DSA #InterviewPreparation #FAANGPreparation #CompetitiveProgramming #StudyPlacement #AdvancedDSA #OrderedSet #CodingInterview