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最新推荐文章于 2025-03-27 16:56:55 发布

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本文介绍了解决两个经典数组问题的方法：一是如何将数组中的元素向右旋转k步；二是如何找到数组中连续子数组的最大和。针对第一个问题，提供了一种原地操作的解决方案，避免了额外内存的使用。对于第二个问题，则通过动态规划的思想实现了线性时间复杂度的高效算法。

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1题目描述：

Rotate an array of n elements to the right by k steps.
For example, with n = 7 and k = 3, the array [1,2,3,4,5,6,7] is rotated to [5,6,7,1,2,3,4].
Note:
Try to come up as many solutions as you can, there are at least 3 different ways to solve this problem.

只能在原地操作，不能添加新内存。

class Solution(object):
    def rotate(self, nums, k):
        """
        :type nums: List[int]
        :type k: int
        :rtype: void Do not return anything, modify nums in-place instead.
        """
        k = k%len(nums)
        nums[:] = nums[-k:]+nums[:-k]

值得注意的是，刚开始用nums = nums[-k:] + nums[:-k]，结果不对！！！这个细节还得研究。

2题目描述：

Find the contiguous subarray within an array (containing at least one number) which has the largest sum.
For example, given the array [-2,1,-3,4,-1,2,1,-5,4],
the contiguous subarray [4,-1,2,1] has the largest sum = 6.

设置暂时最大值与最大值，实时比较大小。时间复杂度O(n)

class Solution(object):
    def maxSubArray(self, nums):
        """
        :type nums: List[int]
        :rtype: int
        """
        if not nums:
            return 0

        curSum = maxSum = nums[0]
        for num in nums[1:]:
            curSum = max(num, curSum + num)
            maxSum = max(maxSum, curSum)

        return maxSum