分治法求最大子段和的伪代码

分治法求解最大子段和（Maximum Subarray Problem）通常使用的是Kadane's Algorithm，这是一个经典的动态规划问题。下面是它的基本伪代码：

function maxSubArraySum(arr, low, high):
    // Base case: If array has only one element
    if low == high:
        return arr[low]

    // Middle index
    mid = (low + high) // 2

    // Recursively find the maximum subarray sum in left and right halves
    max_left_sum = maxSubArraySum(arr, low, mid)
    max_right_sum = maxSubArraySum(arr, mid+1, high)

    // Find the maximum of following three cases
    # a) Maximum subarray with elements till mid
    # b) Maximum subarray with elements after mid
    # c) Maximum subarray with both first and last elements of the current subarray
    max_cross_sum = arr[mid]
    for i from low to mid - 1:
        max_cross_sum = max(max_cross_sum, arr[i] + arr[mid])

    // Return the maximum of these three sums
    return max(max_left_sum, max_right_sum, max_cross_sum)

// Call the function on the given array
arr = [your_array_elements]   // Replace this with your array
n = length of arr
return maxSubArraySum(arr, 0, n-1)

在这个算法中，我们首先检查数组是否只有一个元素，如果是，则直接返回这个元素。然后我们将数组分为两半，分别递归地计算左半部分和右半部分的最大子段和。接着，我们在跨越中间点的情况下寻找最大和，这可以通过遍历左半部分的每个元素并加上中间点的值来完成。最后，我们比较这三个可能的最大和并返回结果。