Before attempting this problem, you should be comfortable with:
We need to count strings of length n where each vowel can only be followed by specific vowels. The rules are: 'a' can be followed by 'e'; 'e' can be followed by 'a' or 'i'; 'i' can be followed by any vowel except itself; 'o' can be followed by 'i' or 'u'; 'u' can be followed by 'a'. We can solve this by exploring all valid paths using recursion, starting from each vowel.
n, return 1 (found a valid string).1 with each of the 5 vowels.10^9 + 7.class Solution:
def countVowelPermutation(self, n: int) -> int:
MOD = 10**9 + 7
follows = {
'a': ['e'],
'e': ['a', 'i'],
'i': ['a', 'e', 'o', 'u'],
'o': ['i', 'u'],
'u': ['a']
}
def dfs(i, v):
if i == n:
return 1
total = 0
for nxt in follows[v]:
total = (total + dfs(i + 1, nxt)) % MOD
return total
res = 0
for vowel in ['a', 'e', 'i', 'o', 'u']:
res = (res + dfs(1, vowel)) % MOD
return resThe plain recursion recomputes the same subproblems many times. For example, counting strings starting with 'e' at position 5 is computed multiple times. We can add memoization to cache results for each (position, vowel) pair. This reduces the time complexity dramatically since there are only O(n * 5) unique states.
(position, vowel).1 if position equals n.1 with each of the 5 vowels.10^9 + 7.class Solution:
def countVowelPermutation(self, n: int) -> int:
MOD = 10**9 + 7
cache = {}
follows = {
'a': ['e'],
'e': ['a', 'i'],
'i': ['a', 'e', 'o', 'u'],
'o': ['i', 'u'],
'u': ['a']
}
def dfs(i, v):
if i == n:
return 1
if (i, v) in cache:
return cache[(i, v)]
total = 0
for nxt in follows[v]:
total = (total + dfs(i + 1, nxt)) % MOD
cache[(i, v)] = total
return total
res = 0
for vowel in ['a', 'e', 'i', 'o', 'u']:
res = (res + dfs(1, vowel)) % MOD
return resInstead of recursion with memoization, we can build the solution iteratively from the base case. We use a 2D DP table where dp[i][v] represents the count of valid strings of length i ending with vowel v. We start with length 1 (each vowel has count 1) and build up to length n by considering which vowels can precede each vowel.
(n+1) x 5.dp[1][v] = 1 for all vowels (there is one string of length 1 for each vowel).i from 2 to n:v, sum up dp[i-1][prev] for all vowels prev that can precede v.dp[n][v] for all vowels v.10^9 + 7.class Solution:
def countVowelPermutation(self, n: int) -> int:
MOD = 10**9 + 7
a, e, i, o, u = 0, 1, 2, 3, 4
dp = [[0] * 5 for _ in range(n + 1)]
dp[1] = [1, 1, 1, 1, 1]
for j in range(2, n + 1):
dp[j][a] = (dp[j - 1][e] + dp[j - 1][i] + dp[j - 1][u]) % MOD
dp[j][e] = (dp[j - 1][a] + dp[j - 1][i]) % MOD
dp[j][i] = (dp[j - 1][e] + dp[j - 1][o]) % MOD
dp[j][o] = dp[j - 1][i] % MOD
dp[j][u] = (dp[j - 1][i] + dp[j - 1][o]) % MOD
return sum(dp[n]) % MODIn the bottom-up approach, we only need the previous row to compute the current row. This means we can reduce space from O(n) to O(1) by using just two arrays of size 5 (or even one array with careful updates). We alternate between the current and previous state arrays.
dp of size 5, all set to 1 (representing strings of length 1).2 to n:next_dp of size 5.v, compute next_dp[v] as the sum of dp[prev] for all valid predecessors.dp with next_dp.dp.10^9 + 7.class Solution:
def countVowelPermutation(self, n: int) -> int:
MOD = 10**9 + 7
follows = [
[1], # 'a' -> 'e'
[0, 2], # 'e' -> 'a', 'i'
[0, 1, 3, 4], # 'i' -> 'a', 'e', 'o', 'u'
[2, 4], # 'o' -> 'i', 'u'
[0] # 'u' -> 'a'
]
dp = [1] * 5
for _ in range(2, n + 1):
next_dp = [0] * 5
for v in range(5):
for nextV in follows[v]:
next_dp[v] = (next_dp[v] + dp[nextV]) % MOD
dp = next_dp
return sum(dp) % MODThe transition between states can be represented as a matrix multiplication. If we define a transition matrix T where T[i][j] = 1 if vowel j can follow vowel i, then multiplying the state vector by T gives the next state. To get the state after n-1 transitions, we compute T^(n-1). Matrix exponentiation allows us to compute this in O(log n) time instead of O(n).
T^(n-1) where T is the transition matrix.1 of each vowel).10^9 + 7.class Solution:
MOD = 10**9 + 7
class M:
def __init__(self, n):
self.a = [[0] * n for _ in range(n)]
def __mul__(self, other):
n = len(self.a)
product = Solution.M(n)
for i in range(n):
for j in range(n):
for k in range(n):
product.a[i][k] = (product.a[i][k] + self.a[i][j] * other.a[j][k]) % Solution.MOD
return product
def matrixExpo(self, base, exp):
n = len(base.a)
result = Solution.M(n)
for i in range(n):
result.a[i][i] = 1
while exp > 0:
if exp % 2 == 1:
result = result * base
base = base * base
exp //= 2
return result
def countVowelPermutation(self, n: int) -> int:
follows = [
[0, 1, 0, 0, 0], # 'a' -> 'e'
[1, 0, 1, 0, 0], # 'e' -> 'a', 'i'
[1, 1, 0, 1, 1], # 'i' -> 'a', 'e', 'o', 'u'
[0, 0, 1, 0, 1], # 'o' -> 'i', 'u'
[1, 0, 0, 0, 0] # 'u' -> 'a'
]
base = Solution.M(5)
base.a = follows
result = self.matrixExpo(base, n - 1)
return sum(sum(row) for row in result.a) % self.MODWhere is the size of the matrix used in matrix exponentiation and is the length of the permutation.
The problem states which vowels can follow each vowel, but the DP recurrence requires knowing which vowels can precede each vowel. Mixing up the direction leads to wrong transition rules.
# Incorrect - using "follows" direction for DP[i][v]
# This computes what can come AFTER v, not what can come BEFORE v
dp[i][a] = dp[i-1][a] # wrong: 'a' follows 'a'?
# Correct - 'a' can be preceded by 'e', 'i', or 'u'
dp[i][a] = dp[i-1][e] + dp[i-1][i] + dp[i-1][u]With large n, intermediate sums overflow before taking the final modulo. Apply modulo after each addition to keep values within bounds.
# Incorrect - sum overflows before modulo
total = 0
for next in follows[v]:
total += dfs(i + 1, next)
return total % MOD
# Correct - apply modulo after each addition
total = 0
for next in follows[v]:
total = (total + dfs(i + 1, next)) % MOD
return totalWhen using matrix exponentiation, computing T^n instead of T^(n-1) is a common error. The initial state already represents strings of length 1, so only n-1 transitions are needed.
# Incorrect - one extra transition
result = matrix_expo(base, n)
# Correct - n-1 transitions from length 1 to length n
result = matrix_expo(base, n - 1)For n=1, the answer is simply 5 (one for each vowel). Some implementations with matrix exponentiation or DP loops may fail if they do not handle this boundary correctly.
# Incorrect - matrix_expo with exp=0 may not return identity
def countVowelPermutation(n):
return sum_matrix(matrix_expo(base, n - 1)) # fails if n=1 and expo not handled
# Correct - either handle n=1 explicitly or ensure expo(M, 0) returns identity
def countVowelPermutation(n):
if n == 1:
return 5
return sum_matrix(matrix_expo(base, n - 1))In languages without arbitrary precision integers (like Java, C++), multiplying two large values before taking modulo causes overflow. Cast to a larger type or restructure the multiplication.
// Incorrect - integer overflow before modulo
product[i][k] = (product[i][k] + a[i][j] * b[j][k]) % MOD;
// Correct - use long long to prevent overflow
product[i][k] = (product[i][k] + (long long)a[i][j] * b[j][k]) % MOD;