[leetcode题后感]Minimum Size Subarray Sum

本文介绍了一种利用二分查找优化的双指针算法来寻找满足条件的最小子序列。该算法适用于解决在数组中寻找和大于等于目标值的最小子序列的问题,并详细解释了其工作原理、复杂度分析以及处理特殊情况的方法。

类似于2sum 用两个指针指向一个subarray

当前subarray的和大于等于目标值时 记录一下当前子序列的长度 若他是最小值则更新最小值 然后讲第一个指针++ 

当子序列的和小于目标值的时候 第二个指针++

依次循环以上过程 直到第二指针走到队尾 或者 当第一个指针大于第二个指针的时候(即数组中出现了一个数字大于目标值) 此时直接返回1即可

要注意集中特殊测试用例

一种是第一个数字就比目标值大的情况

一种是遍历整个数组的和相加都小于目标值的情况

此题为新加入leetcode中的题 

标签中还有一个为二分查找 

这个二分查找的复杂度为nlgn 应该是n方算法的一个优化

具体算法为(转自http://www.cnblogs.com/grandyang/p/4501934.html)

class Solution {
public:
    int minSubArrayLen(int s, vector<int>& nums) {
        int len = nums.size(), sums[len + 1] = {0}, res = len + 1;
        for (int i = 1; i < len + 1; ++i) sums[i] = sums[i - 1] + nums[i - 1];
        for (int i = 0; i < len + 1; ++i) {
            int right = searchRight(i + 1, len, sums[i] + s, sums);
            if (right == len + 1) break;
            if (res > right - i) res = right - i;
        }
        return res == len + 1 ? 0 : res;
    }
    int searchRight(int left, int right, int key, int sums[]) {
        while (left <= right) {
            int mid = (left + right) / 2;
            if (sums[mid] >= key) right = mid - 1;
            else left = mid + 1;
        }
        return left;
    }
};

### LeetCode Top 100 Popular Problems LeetCode provides an extensive collection of algorithmic challenges designed to help developers prepare for technical interviews and enhance their problem-solving skills. The platform categorizes these problems based on popularity, difficulty level, and frequency asked during tech interviews. The following list represents a curated selection of the most frequently practiced 100 problems from LeetCode: #### Array & String Manipulation 1. Two Sum[^2] 2. Add Two Numbers (Linked List)[^2] 3. Longest Substring Without Repeating Characters #### Dynamic Programming 4. Climbing Stairs 5. Coin Change 6. House Robber #### Depth-First Search (DFS) / Breadth-First Search (BFS) 7. Binary Tree Level Order Traversal[^3] 8. Surrounded Regions 9. Number of Islands #### Backtracking 10. Combination Sum 11. Subsets 12. Permutations #### Greedy Algorithms 13. Jump Game 14. Gas Station 15. Task Scheduler #### Sliding Window Technique 16. Minimum Size Subarray Sum 17. Longest Repeating Character Replacement #### Bit Manipulation 18. Single Number[^1] 19. Maximum Product of Word Lengths 20. Reverse Bits This list continues up until reaching approximately 100 items covering various categories including but not limited to Trees, Graphs, Sorting, Searching, Math, Design Patterns, etc.. Each category contains multiple representative questions that cover fundamental concepts as well as advanced techniques required by leading technology companies when conducting software engineering candidate assessments. For those interested in improving logical thinking through gaming activities outside traditional study methods, certain types of video games have been shown beneficial effects similar to engaging directly within competitive coding platforms [^4]. --related questions-- 1. How does participating in online coding competitions benefit personal development? 2. What specific advantages do DFS/BFS algorithms offer compared to other traversal strategies? 3. Can you provide examples illustrating how bit manipulation improves performance efficiency? 4. In what ways might regular participation in programming contests influence job interview success rates? 5. Are there any notable differences between solving problems on paper versus implementing solutions programmatically?
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