923. 3Sum With Multiplicity
Problem Description
Given an integer array arr, and an integer target, return the number of tuples i, j, k such that i < j < k and arr[i] + arr[j] + arr[k] == target.
As the answer can be very large, return it modulo 10^9 + 7.
Example 1:
Input: arr = [1,1,2,2,3,3,4,4,5,5], target = 8
Output: 20
Explanation:
Enumerating by the values (arr[i], arr[j], arr[k]):
(1, 2, 5) occurs 8 times;
(1, 3, 4) occurs 8 times;
(2, 2, 4) occurs 2 times;
(2, 3, 3) occurs 2 times.
Example 2:
Input: arr = [1,1,2,2,2,2], target = 5
Output: 12
Explanation:
arr[i] = 1, arr[j] = arr[k] = 2 occurs 12 times:
We choose one 1 from [1,1] in 2 ways,
and two 2s from [2,2,2,2] in 6 ways.
Constraints:
3 <= arr.length <= 30000 <= arr[i] <= 1000 <= target <= 300
Solution
Python Solution
class Solution:
def threeSumMulti(self, arr: List[int], target: int) -> int:
MOD = 10**9 + 7
count = [0] * 101
for x in arr:
count[x] += 1
ans = 0
for i in range(101):
if count[i] == 0:
continue
for j in range(i, 101):
if count[j] == 0:
continue
k = target - i - j
if k < j or k > 100:
continue
if count[k] == 0:
continue
if i == j == k:
ans += count[i] * (count[i] - 1) * (count[i] - 2) // 6
elif i == j:
ans += count[i] * (count[i] - 1) // 2 * count[k]
elif j == k:
ans += count[i] * count[j] * (count[j] - 1) // 2
else:
ans += count[i] * count[j] * count[k]
return ans % MOD
Time Complexity: O(n + 100^2)
We first count frequencies in O(n), then iterate through possible pairs in O(100^2).
Space Complexity: O(1)
We use a fixed-size array of size 101.
Java Solution
class Solution {
public int threeSumMulti(int[] arr, int target) {
long[] count = new long[101];
for (int x : arr) {
count[x]++;
}
long ans = 0;
for (int i = 0; i <= 100; i++) {
if (count[i] == 0) continue;
for (int j = i; j <= 100; j++) {
if (count[j] == 0) continue;
int k = target - i - j;
if (k < j || k > 100) continue;
if (count[k] == 0) continue;
if (i == j && j == k) {
ans += count[i] * (count[i] - 1) * (count[i] - 2) / 6;
} else if (i == j) {
ans += count[i] * (count[i] - 1) / 2 * count[k];
} else if (j == k) {
ans += count[i] * count[j] * (count[j] - 1) / 2;
} else {
ans += count[i] * count[j] * count[k];
}
}
}
return (int)(ans % (1e9 + 7));
}
}
Time Complexity: O(n + 100^2)
We first count frequencies in O(n), then iterate through possible pairs in O(100^2).
Space Complexity: O(1)
We use a fixed-size array of size 101.
C++ Solution
class Solution {
public:
int threeSumMulti(vector& arr, int target) {
long count[101] = {0};
for (int x : arr) {
count[x]++;
}
long ans = 0;
for (int i = 0; i <= 100; i++) {
if (count[i] == 0) continue;
for (int j = i; j <= 100; j++) {
if (count[j] == 0) continue;
int k = target - i - j;
if (k < j || k > 100) continue;
if (count[k] == 0) continue;
if (i == j && j == k) {
ans += count[i] * (count[i] - 1) * (count[i] - 2) / 6;
} else if (i == j) {
ans += count[i] * (count[i] - 1) / 2 * count[k];
} else if (j == k) {
ans += count[i] * count[j] * (count[j] - 1) / 2;
} else {
ans += count[i] * count[j] * count[k];
}
}
}
return ans % (int)(1e9 + 7);
}
};
Time Complexity: O(n + 100^2)
We first count frequencies in O(n), then iterate through possible pairs in O(100^2).
Space Complexity: O(1)
We use a fixed-size array of size 101.
JavaScript Solution
/**
* @param {number[]} arr
* @param {number} target
* @return {number}
*/
var threeSumMulti = function(arr, target) {
const count = new Array(101).fill(0);
for (const x of arr) {
count[x]++;
}
let ans = 0;
for (let i = 0; i <= 100; i++) {
if (count[i] === 0) continue;
for (let j = i; j <= 100; j++) {
if (count[j] === 0) continue;
const k = target - i - j;
if (k < j || k > 100) continue;
if (count[k] === 0) continue;
if (i === j && j === k) {
ans += count[i] * (count[i] - 1) * (count[i] - 2) / 6;
} else if (i === j) {
ans += count[i] * (count[i] - 1) / 2 * count[k];
} else if (j === k) {
ans += count[i] * count[j] * (count[j] - 1) / 2;
} else {
ans += count[i] * count[j] * count[k];
}
}
}
return ans % (1e9 + 7);
};
Time Complexity: O(n + 100^2)
We first count frequencies in O(n), then iterate through possible pairs in O(100^2).
Space Complexity: O(1)
We use a fixed-size array of size 101.
C# Solution
public class Solution {
public int ThreeSumMulti(int[] arr, int target) {
long[] count = new long[101];
foreach (int x in arr) {
count[x]++;
}
long ans = 0;
for (int i = 0; i <= 100; i++) {
if (count[i] == 0) continue;
for (int j = i; j <= 100; j++) {
if (count[j] == 0) continue;
int k = target - i - j;
if (k < j || k > 100) continue;
if (count[k] == 0) continue;
if (i == j && j == k) {
ans += count[i] * (count[i] - 1) * (count[i] - 2) / 6;
} else if (i == j) {
ans += count[i] * (count[i] - 1) / 2 * count[k];
} else if (j == k) {
ans += count[i] * count[j] * (count[j] - 1) / 2;
} else {
ans += count[i] * count[j] * count[k];
}
}
}
return (int)(ans % (1e9 + 7));
}
}
Time Complexity: O(n + 100^2)
We first count frequencies in O(n), then iterate through possible pairs in O(100^2).
Space Complexity: O(1)
We use a fixed-size array of size 101.