Question

2992. Number of Self-Divisible Permutations

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Description

Given an integer n, return _the number of permutations of the 1-indexed array_ nums = [1, 2, ..., n]_, such that it's self-divisible_.

A 1-indexed array a of length n is self-divisible if for every 1 <= i <= n, gcd(a[i], i) == 1.

A permutation of an array is a rearrangement of the elements of that array, for example here are all of the permutations of the array [1, 2, 3]:

• [1, 2, 3]
• [1, 3, 2]
• [2, 1, 3]
• [2, 3, 1]
• [3, 1, 2]
• [3, 2, 1]

 

Example 1:

Input: n = 1
Output: 1
Explanation: The array [1] has only 1 permutation which is self-divisible.

Example 2:

Input: n = 2
Output: 1
Explanation: The array [1,2] has 2 permutations and only one of them is self-divisible:
nums = [1,2]: This is not self-divisible since gcd(nums[2], 2) != 1.
nums = [2,1]: This is self-divisible since gcd(nums[1], 1) == 1 and gcd(nums[2], 2) == 1.

Example 3:

Input: n = 3
Output: 3
Explanation: The array [1,2,3] has 3 self-divisble permutations: [1,3,2], [3,1,2], [2,3,1].
It can be shown that the other 3 permutations are not self-divisible. Hence the answer is 3.

 

Constraints:

• 1 <= n <= 12

Solutions

Solution 1: State Compression + Memoization Search

We can use a binary number $mask$ to represent the current permutation state, where the $i$-th bit is $1$ indicates that the number $i$ has been used, and $0$ indicates that the number $i$ has not been used yet.

Then, we design a function $dfs(mask)$, which represents the number of permutations that can be constructed from the current permutation state $mask$ and meet the requirements of the problem. The answer is $dfs(0)$.

We can use the method of memoization search to calculate the value of $dfs(mask)$.

In the process of calculating $dfs(mask)$, we use $i$ to indicate which number is to be added to the permutation. If $i \gt n$, it means that the permutation has been constructed, and we can return $1$.

Otherwise, we enumerate the numbers $j$ that have not been used in the current permutation. If $i$ and $j$ meet the requirements of the problem, then we can add $j$ to the permutation. At this time, the state becomes $mask \mid 2^j$, where $|$ represents bitwise OR operation. Since $j$ has been used, we need to recursively calculate the value of $dfs(mask \mid 2^j)$ and add it to $dfs(mask)$.

Finally, we can get the value of $dfs(0)$, which is the answer.

The time complexity is $O(n \times 2^n)$, and the space complexity is $O(2^n)$. Where $n$ is the length of the permutation.

class Solution:
  def selfDivisiblePermutationCount(self, n: int) -> int:
    @cache
    def dfs(mask: int) -> int:
      i = mask.bit_count() + 1
      if i > n:
        return 1
      ans = 0
      for j in range(1, n + 1):
        if (mask >> j & 1) == 0 and (i % j == 0 or j % i == 0):
          ans += dfs(mask | 1 << j)
      return ans

    return dfs(0)

class Solution {
  private int n;
  private Integer[] f;

  public int selfDivisiblePermutationCount(int n) {
    this.n = n;
    f = new Integer[1 << (n + 1)];
    return dfs(0);
  }

  private int dfs(int mask) {
    if (f[mask] != null) {
      return f[mask];
    }
    int i = Integer.bitCount(mask) + 1;
    if (i > n) {
      return 1;
    }
    f[mask] = 0;
    for (int j = 1; j <= n; ++j) {
      if ((mask >> j & 1) == 0 && (i % j == 0 || j % i == 0)) {
        f[mask] += dfs(mask | 1 << j);
      }
    }
    return f[mask];
  }
}

class Solution {
public:
  int selfDivisiblePermutationCount(int n) {
    int f[1 << (n + 1)];
    memset(f, -1, sizeof(f));
    function<int(int)> dfs = [&](int mask) {
      if (f[mask] != -1) {
        return f[mask];
      }
      int i = __builtin_popcount(mask) + 1;
      if (i > n) {
        return 1;
      }
      f[mask] = 0;
      for (int j = 1; j <= n; ++j) {
        if ((mask >> j & 1) == 0 && (i % j == 0 || j % i == 0)) {
          f[mask] += dfs(mask | 1 << j);
        }
      }
      return f[mask];
    };
    return dfs(0);
  }
};

func selfDivisiblePermutationCount(n int) int {
  f := make([]int, 1<<(n+1))
  for i := range f {
    f[i] = -1
  }
  var dfs func(int) int
  dfs = func(mask int) int {
    if f[mask] != -1 {
      return f[mask]
    }
    i := bits.OnesCount(uint(mask)) + 1
    if i > n {
      return 1
    }
    f[mask] = 0
    for j := 1; j <= n; j++ {
      if mask>>j&1 == 0 && (i%j == 0 || j%i == 0) {
        f[mask] += dfs(mask | 1<<j)
      }
    }
    return f[mask]
  }
  return dfs(0)
}

function selfDivisiblePermutationCount(n: number): number {
  const f: number[] = Array(1 << (n + 1)).fill(-1);
  const dfs = (mask: number): number => {
    if (f[mask] !== -1) {
      return f[mask];
    }
    const i = bitCount(mask) + 1;
    if (i > n) {
      return 1;
    }
    f[mask] = 0;
    for (let j = 1; j <= n; ++j) {
      if (((mask >> j) & 1) === 0 && (i % j === 0 || j % i === 0)) {
        f[mask] += dfs(mask | (1 << j));
      }
    }
    return f[mask];
  };
  return dfs(0);
}

function bitCount(i: number): number {
  i = i - ((i >>> 1) & 0x55555555);
  i = (i & 0x33333333) + ((i >>> 2) & 0x33333333);
  i = (i + (i >>> 4)) & 0x0f0f0f0f;
  i = i + (i >>> 8);
  i = i + (i >>> 16);
  return i & 0x3f;
}

Solution 2: State Compression + Dynamic Programming

We can rewrite the memoization search in Solution 1 into the form of dynamic programming, define $f[mask]$ to represent the number of permutations that the current permutation state is $mask$ and meet the requirements of the problem. Initially, $f[0]=1$, and the rest are $0$.

We enumerate $mask$ in the range of $[0, 2^n)$, for each $mask$, we use $i$ to represent which number is the last one to join the permutation, then we enumerate the last number $j$ added to the current permutation. If $i$ and $j$ meet the requirements of the problem, then the state $f[mask]$ can be transferred from the state $f[mask \oplus 2^(j-1)]$, where $\oplus$ represents bitwise XOR operation. We add all the values of the transferred state $f[mask \oplus 2^(j-1)]$ to $f[mask]$, which is the value of $f[mask]$.

Finally, we can get the value of $f[2^n - 1]$, which is the answer.

The time complexity is $O(n \times 2^n)$, and the space complexity is $O(2^n)$. Where $n$ is the length of the permutation.

class Solution:
  def selfDivisiblePermutationCount(self, n: int) -> int:
    f = [0] * (1 << n)
    f[0] = 1
    for mask in range(1 << n):
      i = mask.bit_count()
      for j in range(1, n + 1):
        if (mask >> (j - 1) & 1) == 1 and (i % j == 0 or j % i == 0):
          f[mask] += f[mask ^ (1 << (j - 1))]
    return f[-1]

class Solution {
  public int selfDivisiblePermutationCount(int n) {
    int[] f = new int[1 << n];
    f[0] = 1;
    for (int mask = 0; mask < 1 << n; ++mask) {
      int i = Integer.bitCount(mask);
      for (int j = 1; j <= n; ++j) {
        if (((mask >> (j - 1)) & 1) == 1 && (i % j == 0 || j % i == 0)) {
          f[mask] += f[mask ^ (1 << (j - 1))];
        }
      }
    }
    return f[(1 << n) - 1];
  }
}

class Solution {
public:
  int selfDivisiblePermutationCount(int n) {
    int f[1 << n];
    memset(f, 0, sizeof(f));
    f[0] = 1;
    for (int mask = 0; mask < 1 << n; ++mask) {
      int i = __builtin_popcount(mask);
      for (int j = 1; j <= n; ++j) {
        if (((mask >> (j - 1)) & 1) == 1 && (i % j == 0 || j % i == 0)) {
          f[mask] += f[mask ^ (1 << (j - 1))];
        }
      }
    }
    return f[(1 << n) - 1];
  }
};

func selfDivisiblePermutationCount(n int) int {
  f := make([]int, 1<<n)
  f[0] = 1
  for mask := 0; mask < 1<<n; mask++ {
    i := bits.OnesCount(uint(mask))
    for j := 1; j <= n; j++ {
      if mask>>(j-1)&1 == 1 && (i%j == 0 || j%i == 0) {
        f[mask] += f[mask^(1<<(j-1))]
      }
    }
  }
  return f[(1<<n)-1]
}

function selfDivisiblePermutationCount(n: number): number {
  const f: number[] = Array(1 << n).fill(0);
  f[0] = 1;
  for (let mask = 0; mask < 1 << n; ++mask) {
    const i = bitCount(mask);
    for (let j = 1; j <= n; ++j) {
      if ((mask >> (j - 1)) & 1 && (i % j === 0 || j % i === 0)) {
        f[mask] += f[mask ^ (1 << (j - 1))];
      }
    }
  }
  return f.at(-1)!;
}

function bitCount(i: number): number {
  i = i - ((i >>> 1) & 0x55555555);
  i = (i & 0x33333333) + ((i >>> 2) & 0x33333333);
  i = (i + (i >>> 4)) & 0x0f0f0f0f;
  i = i + (i >>> 8);
  i = i + (i >>> 16);
  return i & 0x3f;
}