# Closest Subsequence Sum

## Problem

You are given an integer array $nums$ and an integer $goal$.

You want to choose a subsequence of $nums$ such that the sum of its elements is the closest possible to $goal$. That is, if the sum of the subsequence’s elements is $sum$, then you want to minimize the absolute difference $abs(sum - goal)$.

Return the minimum possible value of $abs(sum - goal)$.

Note that a subsequence of an array is an array formed by removing some elements (possibly all or none) of the original array.

### Example

Input: nums = [5,-7,3,5], goal = 6
Output: 0
Explanation: Choose the whole array as a subsequence, with a sum of 6.
This is equal to the goal, so the absolute difference is 0.
Input: nums = [7,-9,15,-2], goal = -5
Output: 1
Explanation: Choose the subsequence [7,-9,-2], with a sum of -4.
The absolute difference is abs(-4 - (-5)) = abs(1) = 1, which is the minimum.
Input: nums = [1,2,3], goal = -7
Output: 7

### Constraints

• $1 \leq nums.length \leq 40$
• $-10^7 \leq nums_i \leq 10^7$
• $-10^9 \leq goal \leq 10^9$

## Solution

### Full search brute force (TLE)

First idea come to my mind is Brute Force because $n \leq 40$ is pretty small, I can even get away with $O(n^4)$. So I made an array of set $f$ to maintain all possible $sum$. $f_i$ holds all $sum$ candidates until $nums_i$. Here is the code for your reference

class Solution {
public:
int minAbsDifference(vector<int>& nums, int goal) {
sort(nums.begin(), nums.end());
int n = nums.size();
vector<set<int>> f(n+1);
f.insert(0);
int ret = abs(goal);
for(int i = 1;i<=n;i++) {
for(auto x: f[i-1]) {
int score;
f[i].insert(x);
score = abs(nums[i-1] - goal);
if(score < ret || nums[i-1] < goal) {
ret = min(ret, score);
f[i].insert(nums[i-1]);
}
auto sum = x + nums[i-1];
score = abs(sum - goal);
if(score < ret || sum < goal) {
ret = min(ret, score);
f[i].insert(sum);
}
}
}
return ret;
}
};

Sadly this gives TLE, I can optimize it further by using only 1 sets instead of $n$ sets but I don’t think it would AC.

### Intuition

So I changed my approach, notice that $n = 40$ so $O(2^n) = T(2^{40})$ would very likely to TLE, but how about $T(2^{20})$? Seems sensible. The idea is simple:

• Split $nums$ into 2 array, let’s call them $left$ and $right$
• Calculate all possible $sum$ for each sub array. Sort them.
• For each $a \in left$, find $b \in right$ so that $a+b$ is closest to $goal$. This could be done in $O(m \times log(m))$ with $m$ being the candidates array length $2^n$
• Altogether we have an $O(2^n \times n)$ solution. It gives AC but I was afraid it is still slow I made a small optimization to reduce search range while traversing $right$ array.

### Complexity

• Time complexity: $O(2^n \times n)$
• Space complexity: $O(2^n)$

## Code

class Solution {
public:
void buildPermutation(vector<int>& nums, vector<int>& a, int i, int n) {
int k = n-i;
int size = (1<<k);
a.resize(size,0);
for(int j = 0;j<size;j++) {
for(int b = 0;b<k;b++) {
if((1<<b)&j) {
continue;
}
int next = (1<<b)|j;
a[next] += nums[i+b];
}
}
sort(a.begin(), a.end());
}
int minAbsDifference(vector<int>& nums, int goal) {
int n = nums.size();
int m = n/2;
vector<int> left, right;
buildPermutation(nums, left, 0, m);
buildPermutation(nums, right, m, n);
int ret = 2e9+1;
int j = right.size();
for(auto a: left) {
int target = goal - a;
j = distance(right.begin(), lower_bound(right.begin(), right.begin()+j, target));
if(j < right.size()) {
int b = right[j];
ret = min(ret, abs(a + b - goal));
}
if(j != 0) {
int b = right[j-1];
ret = min(ret, abs(a + b - goal));
}
if(ret == 0) {
break;
}
}
return ret;
}
};

That's all! Thank you for reading all the way here 😊

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