#1016

Medium

auto_awesomeSliding window with running state updates

LeetCode Problem Workspace

Binary String With Substrings Representing 1 To N

Check if binary string contains all integers from 1 to n as substrings, leveraging sliding window and bit manipulation techniques.

hash table string bit manipulation sliding window

Problem Statement

Given a binary string s and an integer n, you are tasked with checking if all binary representations of integers in the range [1, n] exist as substrings in s. The binary representation of a number is a sequence of '0's and '1's that represents the number in base-2 format.

For example, if s = '0110' and n = 3, then the binary representations of 1, 2, and 3 are '1', '10', and '11' respectively. The string '0110' contains all these substrings, so the output is true. For n = 4, the binary representation '100' is missing from '0110', so the output is false.

Examples

Example 1

Input: s = "0110", n = 3

Output: true

Example details omitted.

Example 2

Input: s = "0110", n = 4

Output: false

Example details omitted.

Constraints

1 <= s.length <= 1000
s[i] is either '0' or '1'.
1 <= n <= 109

Solution Approach

Sliding Window with Running State Updates

The key to solving this problem efficiently lies in utilizing a sliding window to check substrings of length at most 30. The reason for this is that any number greater than 2^30 will have more than 30 bits, making the substrings of length 30 sufficient for all practical purposes. By maintaining a set or hash table of found binary representations, we can track the progress while moving through the string.

Bit Manipulation for Binary Representations

Since the problem revolves around checking binary representations, bit manipulation can simplify and speed up checking whether the required numbers appear as substrings. Instead of converting each number to a string manually, bitwise operations can help generate the binary form directly, allowing faster comparisons against the substrings in the binary string.

Efficient Hashing and Lookup

Using a hash table to store the binary representations of numbers from 1 to n allows us to efficiently check if these substrings appear in the given binary string. By using hashing for quick lookups, we avoid having to repeatedly search through the string, making the solution more scalable and reducing unnecessary comparisons.

Complexity Analysis

Metric	Value
Time	Depends on the final approach
Space	Depends on the final approach

The time complexity depends on the final approach, but with a sliding window and hashing, the solution should run in linear time relative to the length of the binary string. The space complexity will primarily depend on the number of substrings stored in the hash table, which is at most n or the number of required substrings.

What Interviewers Usually Probe

Candidate demonstrates an understanding of sliding window techniques.
Candidate leverages bit manipulation for optimizing binary substring checks.
Candidate uses a hash table effectively for substring tracking and lookups.

Common Pitfalls or Variants

Common pitfalls

Failing to recognize that only substrings of length at most 30 are needed due to binary number size constraints.
Not efficiently checking for the presence of all required substrings by brute force.
Overcomplicating the problem by trying to check all possible substrings without using efficient data structures like hash tables.

Follow-up variants

What if the range of numbers is extremely large? Can the approach be adapted for larger ranges efficiently?
Can this approach work if the binary string is not guaranteed to be contiguous or has interruptions?
What happens if we need to handle non-binary strings or strings with different character sets?

FAQ

What is the main technique used in solving the Binary String With Substrings Representing 1 To N problem?

The main technique involves using a sliding window combined with running state updates and bit manipulation for efficient substring checks.

How do we optimize the substring checking in this problem?

By limiting the length of the substrings to at most 30 bits, which corresponds to the binary representations of numbers up to 2^30, and using a hash table for efficient lookup.

Can we use any other data structures to solve this problem?

Yes, a hash set or a hash map can be used to store the binary substrings and perform fast lookups while iterating through the string.

Why is the sliding window approach particularly useful for this problem?

The sliding window allows us to efficiently check for substrings without re-checking the entire string multiple times, optimizing the solution.

What is the time complexity of this solution?

The time complexity is linear with respect to the length of the binary string, depending on the approach chosen for sliding window and hash table lookups.

terminal

Solution

Solution 1: Brain Teaser

We observe that the length of string $s$ does not exceed $1000$, so string $s$ can represent at most $1000$ binary integers. Therefore, if $n \gt 1000$, then $s$ definitely cannot represent the binary representation of all integers in the range $[1,.. n]$.

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class Solution:
    def queryString(self, s: str, n: int) -> bool:
        if n > 1000:
            return False
        return all(bin(i)[2:] in s for i in range(n, n // 2, -1))

Continue Practicing

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