This article introduces how to solve algorithm problems using the sliding window technique. The sliding window is an effective technique for various array and string-related problems, reducing time complexity and providing efficient solutions.<\/p>\n<\/header>\n Given an array as follows, let’s solve the problem of finding the length of the longest subarray whose sum does not exceed K.<\/p>\n By utilizing the sliding window technique, we traverse the array while maintaining the current sum, recording the maximum length when this sum does not exceed K. If the current sum exceeds K, we increment the starting index to reduce the sum and find a point that satisfies the condition.<\/p>\n<\/section>\n Now let’s look at each part of the code.<\/p>\nProblem Description<\/h2>\n
\n Input: nums = [1,2,3,4,5,6], K = 10\n Output: 4\n Explanation: [1,2,3,4] is the longest subarray whose sum does not exceed 10.\n <\/pre>\n<\/section>\n
Problem Approach<\/h2>\n
Sliding Window Algorithm Implementation<\/h2>\n
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\n public class Solution {\n public int MaxLengthSubArray(int[] nums, int K) {\n int left = 0, maxLength = 0, currentSum = 0;\n\n for (int right = 0; right < nums.Length; right++) {\n currentSum += nums[right];\n\n while (currentSum > K) {\n currentSum -= nums[left];\n left++;\n }\n\n maxLength = Math.Max(maxLength, right - left + 1);\n }\n\n return maxLength;\n }\n }\n <\/code>\n <\/pre>\n<\/section>\nCode Explanation<\/h2>\n
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left<\/code>, maxLength<\/code>, and currentSum<\/code> variables.<\/li>\nright<\/code> to traverse the array. Add the current number to currentSum<\/code>.<\/li>\nleft<\/code>, subtracting the corresponding number from currentSum<\/code>, and repeat this process until the sum is less than or equal to K.<\/li>\nmaxLength<\/code>.<\/li>\n<\/ul>\n<\/section>\nComplexity Analysis<\/h2>\n