I recently got a subscription to Leetcode.com. I found this challenge particularly challenging:

## Challenge

Given an array nums and a target value k, find the maximum length of a subarray that sums to k. If there isn’t one, return 0 instead.

Note: The sum of the entire nums array is guaranteed to fit within the 32-bit signed integer range.

### Example 1:

Input: nums = [1, -1, 5, -2, 3], k = 3
Output: 4


Explanation: The subarray [1, -1, 5, -2] sums to 3 and is the longest.

### Example 2:

Input: nums = [-2, -1, 2, 1], k = 1
Output: 2


Explanation: The subarray [-1, 2] sums to 1 and is the longest.

### Solution:

When solving this challenge I made several mistakes. The challenge is asking us to determine the maximum sub-array length. Not the maximum sub-sequence length. A sub-array is contigous. This is a key note to solving the challenge.

#### First Attempt

In my first attempt I used a brute force algorithm. Where I determined every possible sub-array storing only the ones where the sum was equal to the given amount.

This strategy proved to be very ineffecient. As it requires several iterations to determine the output.

1. First iteration determines every possible sub-array
2. Second iteration for every sub-array
3. Third iteration for the array of sub-arrays to determine the one with the longest length
def max_sub_array_len(nums, k)
sub_arrays = []
nums.each_with_index do |num, index|
slice_length = 0
while slice_length <= nums.length
sub_array = nums.slice(index, slice_length)
sum = sub_array.inject(:+)
if sum == k
sub_arrays << sub_array
end

slice_length += 1
end
end

sub_arrays.map(&:length).max || 0
end


#### Second Attempt

This soluion is far more efficient since we are iterating through the elements only once.

def max_sub_array_len(nums, k)
sum = 0
max_length = 0
history = {0 => -1}

nums.each_with_index do |num, index|
sum += num
history[sum] ||= index

if history[sum - k]
max_length = [max_length, index - history[sum - k]].max
end
end

max_length
end


With this solution we iterate through nums and for every element we store the current sum and current index.

Let’s walk through this solution given the following inputs.

nums = [1, 1, 5, -2, 3]
k = 3


As we iterate through the nums the sum and index are as follows.

1  2  7   5  8  # sums
1, 1, 5, -2, 3  # nums
0  1  2   3  4  # index


We are trying to find the longest sub_array with a sum of 3. Given the nums input we are given there is only one sub_array with the sum of 3.

[5, -2]


When iterating over the nums it is at index 3 when iterating over -2 we will determine the only correct sub_array.

Can you see why?

At index 3 we know the following:

sum = 5
k = 3


We need to know if at any point a recorded sum is equal to the difference of sum and k (3). Let’s look at our table again. Ah, okay we can see that at index 1 the sum was 2.

1  2  7   5  8  # sums
1, 1, 5, -2, 3  # nums
0  1  2   3  4  # index


So we know our sub_array must contain all elements to the left of our current element stopping before index 1.

The correct sub_array is then [5, -2] with a sum equal to the k input of 3. And the correct max_length determined is 2.