time limit exceeded
largest =  if len(nums) == 1: return nums if len(nums) <1001: for n in range(len(nums)): subArr =  i = n while i < len(nums): i2 = 0 if nums[i] <=0: stock =  for i2 in range(i+1,len(nums)): stock.append(nums[i2]) if nums[i] + sum(stock)>0: subArr.append(nums[i]) for n in range(len(stock)): subArr.append(stock[n]) i = i2 break else: subArr.append(nums[i]) if not subArr: subArr.append(nums[i]) if i2 == len(nums)-1: i = i2 i +=1 if not largest: largest = subArr else: if sum(largest) < sum(subArr): largest = subArr return sum(largest)
@ImTheOne It seems really confusing. I am not sure what your algorithm is but I see three for loops and I imagine they have a time restriction to O(n^2) for the problem. Thus your solution fails the bounds set. :(
Try using a known algorithm/strategy like DP or Divide and Conquer which have time complexities of O(n) and O(n lg n) respectively.