LeetCode | Tutorial Minimum Size Subarray Sum Problem Java Code | Sliding Window | O(n) Complexity
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Welcome to this YouTube tutorial
Welcome to this YouTube tutorial on solving the Minimum Size Subarray Sum problem on LeetCode using Java and the sliding window technique. In this problem, you are given an array of positive integers, 'nums', and a positive integer, 'target'. Your task is to find the minimal length of a subarray whose sum is greater than or equal to the target. If no such subarray exists, the function should return 0.
In this tutorial, we will walk you through the step-by-step process of solving this problem efficiently in O(n) complexity using the sliding window technique. We will explain the problem statement and provide intuitive examples to help you understand the concept.
Example 1:
Input: target = 7, nums = [2,3,1,2,4,3]
Output: 2
Explanation: The subarray [4,3] has the minimal length (2) that satisfies the condition.
Example 2:
Input: target = 4, nums = [1,4,4]
Output: 1
Example 3:
Input: target = 11, nums = [1,1,1,1,1,1,1,1]
Output: 0
To solve this problem efficiently, we will utilize the sliding window technique, which allows us to maintain a window of elements in the array and adjust it based on the given conditions. We will demonstrate how to implement the solution using Java code, explaining each step along the way.
Whether you're preparing for coding interviews or looking to improve your algorithmic problem-solving skills, this tutorial is perfect for you. Watch the full video to grasp the concept of the sliding window technique and master solving the Minimum Size Subarray Sum problem on LeetCode.
Don't forget to like the video, subscribe to the channel, and leave a comment if you have any questions or suggestions. Let's dive into the fascinating world of algorithms together!
#LeetCode #JavaTutorial #SlidingWindow #Algorithm #CodingInterview
In this tutorial, we will walk you through the step-by-step process of solving this problem efficiently in O(n) complexity using the sliding window technique. We will explain the problem statement and provide intuitive examples to help you understand the concept.
Example 1:
Input: target = 7, nums = [2,3,1,2,4,3]
Output: 2
Explanation: The subarray [4,3] has the minimal length (2) that satisfies the condition.
Example 2:
Input: target = 4, nums = [1,4,4]
Output: 1
Example 3:
Input: target = 11, nums = [1,1,1,1,1,1,1,1]
Output: 0
To solve this problem efficiently, we will utilize the sliding window technique, which allows us to maintain a window of elements in the array and adjust it based on the given conditions. We will demonstrate how to implement the solution using Java code, explaining each step along the way.
Whether you're preparing for coding interviews or looking to improve your algorithmic problem-solving skills, this tutorial is perfect for you. Watch the full video to grasp the concept of the sliding window technique and master solving the Minimum Size Subarray Sum problem on LeetCode.
Don't forget to like the video, subscribe to the channel, and leave a comment if you have any questions or suggestions. Let's dive into the fascinating world of algorithms together!
#LeetCode #JavaTutorial #SlidingWindow #Algorithm #CodingInterview
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