. Target Sum

494. Target Sum

Difficulty: Medium

Topics: Array, Dynamic Programming, Backtracking

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

Solution:

The "Target Sum" problem involves creating expressions using the numbers in an array nums by assigning a or - sign to each number. The goal is to calculate how many such expressions evaluate to the given target. This problem can be solved efficiently using dynamic programming or backtracking.

Key Points

1. Input Constraints:
   • Array length: 1 <= nums.length <= 20
   • Element values: 0 <= nums[i] <= 1000
   • Target range: -1000 <= target <= 1000

2. Output:

   • Return the count of expressions that evaluate to the target.

3. Challenges:

   • The solution must handle both small and large values of target.
   • Efficient handling of up to 2²⁰ combinations when using backtracking.

Approach

We can solve this problem using Dynamic Programming (Subset Sum Transformation) or Backtracking. Below, we use Dynamic Programming (DP) for efficiency.

Key Observations:

1. If we split nums into two groups: P (positive subset) and N (negative subset), then: target = sum(P) - sum(N)

Rearrange terms: sum(P) sum(N) = sum(nums)

Let S be the sum of the positive subset. Then: S = (sum(nums) target) / 2

1. If (sum(nums) target) % 2 ≠ 0, return 0 because it's impossible to partition the sums.

Plan

1. Compute the total sum of nums.
2. Check if the target is achievable using the formula for S . If invalid, return 0.
3. Use a DP approach to find the count of subsets in nums whose sum equals S .

Let's implement this solution in PHP: 494. Target Sum

<?php
/**
 * @param Integer[] $nums
 * @param Integer $target
 * @return Integer
 */
function findTargetSumWays($nums, $target) {
    ...
    ...
    ...
    /**
     * go to ./solution.php
     */
}

// Example usage:
$nums = [1, 1, 1, 1, 1];
$target = 3;
echo findTargetSumWays($nums, $target); // Output: 5
?>

Explanation:

1. Input Validation:

   • We calculate S = (sum(nums) target) / 2.
   • If S is not an integer, it's impossible to split nums into two subsets.

2. Dynamic Programming Logic:

   • dp[j] represents the number of ways to form a sum j using the given numbers.
   • Starting with dp[0] = 1, for each number in nums, we update dp[j] by adding the count of ways to form j - num.

3. Result:

   • After processing all numbers, dp[S] contains the count of ways to achieve the target sum.

Example Walkthrough

Input: nums = [1, 1, 1, 1, 1], target = 3

1. totalSum = 5, S = (5 3) / 2 = 4.
2. Initialize DP array: dp = [1, 0, 0, 0, 0].
3. Process each number:
   • For 1: Update dp: [1, 1, 0, 0, 0].
   • For 1: Update dp: [1, 2, 1, 0, 0].
   • For 1: Update dp: [1, 3, 3, 1, 0].
   • For 1: Update dp: [1, 4, 6, 4, 1].
   • For 1: Update dp: [1, 5, 10, 10, 5].
4. Result: dp[4] = 5.

Time Complexity

• Time: O(n x S), where n is the length of nums and S is the target sum.
• Space: O(S) for the DP array.

Output for Example

Input: nums = [1,1,1,1,1], target = 3

Output: 5

This approach efficiently calculates the number of ways to form the target sum using dynamic programming. By reducing the problem to subset sum, we leverage the structure of DP for better performance.

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