Given a non-negative integer N return the number of binary strings of length N without 2 consecutive ones
Example:
Input: N = 3 Output: 5 Explanation: [ "000", "001", "010", "100", "101", ]Notes: N <= 30
The core challenge of this problem is to generate binary strings of a given length N such that no two consecutive '1's appear in the string. This problem is significant in various applications such as coding theory, data transmission, and error detection where consecutive ones might be problematic.
Potential pitfalls include misunderstanding the constraints and generating strings that do not meet the criteria.
To solve this problem, we can use dynamic programming. The idea is to build the solution for length N using the solutions for smaller lengths.
Let's define two arrays:
dp0[i]
: Number of binary strings of length i
ending with '0'dp1[i]
: Number of binary strings of length i
ending with '1'The recurrence relations are:
dp0[i] = dp0[i-1] + dp1[i-1]
(We can append '0' to any string of length i-1
)dp1[i] = dp0[i-1]
(We can only append '1' to strings of length i-1
ending with '0')The total number of valid strings of length N
is dp0[N] + dp1[N]
.
1. Initialize dp0[1]
and dp1[1]
to 1 since there are two valid strings of length 1: "0" and "1".
2. Iterate from 2 to N, updating dp0[i]
and dp1[i]
using the recurrence relations.
3. Return the sum of dp0[N]
and dp1[N]
.
public class BinaryStringsWithoutConsecutiveOnes {
public static int countBinaryStrings(int N) {
if (N == 0) return 1;
if (N == 1) return 2;
// Arrays to store the number of valid strings ending with 0 and 1
int[] dp0 = new int[N + 1];
int[] dp1 = new int[N + 1];
// Base cases
dp0[1] = 1;
dp1[1] = 1;
// Fill the dp arrays
for (int i = 2; i <= N; i++) {
dp0[i] = dp0[i - 1] + dp1[i - 1];
dp1[i] = dp0[i - 1];
}
// The total number of valid strings of length N
return dp0[N] + dp1[N];
}
public static void main(String[] args) {
int N = 3;
System.out.println("Number of binary strings of length " + N + " without consecutive ones: " + countBinaryStrings(N));
}
}
The time complexity of this approach is O(N) because we iterate from 2 to N, performing constant-time operations in each iteration. The space complexity is also O(N) due to the storage required for the dp0
and dp1
arrays.
Potential edge cases include:
N = 0
: The output should be 1 (the empty string).N = 1
: The output should be 2 (strings "0" and "1").To test the solution comprehensively, consider the following test cases:
N = 0
: Expected output is 1.N = 1
: Expected output is 2.N = 2
: Expected output is 3 (strings "00", "01", "10").N = 3
: Expected output is 5 (strings "000", "001", "010", "100", "101").When approaching such problems:
Understanding and solving problems like this one helps improve your dynamic programming skills and your ability to recognize patterns in problems. Practice is key to mastering these concepts.
For further reading and practice, consider the following resources: