2466. Count Ways To Build Good Strings
Description
Given the integers zero, one, low, and high, we can construct a string by starting with an empty string, and then at each step perform either of the following:
- Append the character
'0'zerotimes. - Append the character
'1'onetimes.
This can be performed any number of times.
A good string is a string constructed by the above process having a length between low and high (inclusive).
Return the number of different good strings that can be constructed satisfying these properties. Since the answer can be large, return it modulo 109 + 7.
Example 1:
Input: low = 3, high = 3, zero = 1, one = 1 Output: 8 Explanation: One possible valid good string is "011". It can be constructed as follows: "" -> "0" -> "01" -> "011". All binary strings from "000" to "111" are good strings in this example.
Example 2:
Input: low = 2, high = 3, zero = 1, one = 2 Output: 5 Explanation: The good strings are "00", "11", "000", "110", and "011".
Constraints:
1 <= low <= high <= 1051 <= zero, one <= low
Solutions
Solution 1: Memoization Search
Thinking
Each step appends \(zero\) zeros or \(one\) ones; a string is good if its length lies in \([low,high]\). With \(high\le 10^5\), \(dfs(i)\) is the number of ways after length \(i\): count \(1\) if \(i\) is already in range, then add \(dfs(i+zero)\) and \(dfs(i+one)\).
We design a function \(dfs(i)\) to represent the number of good strings constructed starting from the \(i\)-th position. The answer is \(dfs(0)\).
The computation process of the function \(dfs(i)\) is as follows:
- If \(i > high\), return \(0\);
- If \(low \leq i \leq high\), increment the answer by \(1\), then after \(i\), we can add either
zeronumber of \(0\)s oronenumber of \(1\)s. Therefore, the answer is incremented by \(dfs(i + zero) + dfs(i + one)\).
During the process, we need to take the modulus of the answer, and we can use memoization search to reduce redundant computations.
The time complexity is \(O(n)\), and the space complexity is \(O(n)\). Here, \(n = high\).
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Solution 2: Dynamic programming
Thinking
Method 1 recurses on the current length. Let \(f[i]\) be ways to reach length \(i\), \(f[0]=1\), from \(f[i-zero]\) and \(f[i-one]\), then sum \(f\) on \([low,high]\). No recursion stack.
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