# Solving Leetcode Interviews in Seconds with AI: Target Sum


	# Introduction
	In this blog post, we will explore how to solve the LeetCode problem "494" using AI. LeetCode is a popular platform for preparing for coding interviews, and with the help of AI tools like [Chatmagic](https://www.chatmagic.app), we can generate solutions quickly and efficiently - helping you pass the interviews and get the job offer without having to study for months.

	# Problem Statement
	> You are given an integer array nums and an integer target. You want to build an expression out of nums by adding one of the symbols '+' and '-' before each integer in nums and then concatenate all the integers.  For example, if nums = [2, 1], you can add a '+' before 2 and a '-' before 1 and concatenate them to build the expression "+2-1".  Return the number of different expressions that you can build, which evaluates to target.   Example 1:  Input: nums = [1,1,1,1,1], target = 3 Output: 5 Explanation: There are 5 ways to assign symbols to make the sum of nums be target 3. -1 + 1 + 1 + 1 + 1 = 3 +1 - 1 + 1 + 1 + 1 = 3 +1 + 1 - 1 + 1 + 1 = 3 +1 + 1 + 1 - 1 + 1 = 3 +1 + 1 + 1 + 1 - 1 = 3  Example 2:  Input: nums = [1], target = 1 Output: 1    Constraints:  1 <= nums.length <= 20 0 <= nums[i] <= 1000 0 <= sum(nums[i]) <= 1000 -1000 <= target <= 1000  

	# Explanation
	Here's the breakdown of the solution and the Python code:

*   **High-Level Approach:**

    *   The problem can be transformed into a subset sum problem. Instead of assigning '+' or '-', we can think of dividing the array into two subsets: one where we add the numbers (positive subset) and one where we subtract the numbers (negative subset).
    *   The sum of the positive subset (sum\_positive) and the sum of the negative subset (sum\_negative) must satisfy sum\_positive - sum\_negative = target. Also, sum\_positive + sum\_negative = sum(nums).  Solving for sum\_positive, we get sum\_positive = (target + sum(nums)) / 2.
    *   The problem now becomes finding the number of subsets of nums that sum up to sum\_positive. We can solve this using dynamic programming.

*   **Complexity:**
    *   Runtime: O(n * sum), where n is the length of `nums` and sum is the sum of elements in `nums`.
    *   Storage: O(sum)

	
	# Code
	```python
	def findTargetSumWays(nums, target):
    """
    Finds the number of different expressions that can be built from nums
    to evaluate to target.
    """
    total_sum = sum(nums)

    # If the target is unreachable
    if (total_sum + target) < 0 or (total_sum + target) % 2 != 0:
        return 0

    subset_sum = (total_sum + target) // 2

    # Initialize DP table
    dp = [0] * (subset_sum + 1)
    dp[0] = 1  # Empty subset sums to 0 in one way

    # Iterate over numbers
    for num in nums:
        for j in range(subset_sum, num - 1, -1):  # Iterate backwards to avoid overcounting
            dp[j] += dp[j - num]

    return dp[subset_sum]
	```