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3399. Smallest Substring With Identical Characters II


Problem Description

You are given a binary string s of length n and an integer numOps. You are allowed to perform the following operation on s at most numOps times:

  • Select any index i (where 0 <= i < n) and flip s[i]. If s[i] == '1', change s[i] to '0' and vice versa.

You need to minimize the length of the longest substring of s such that all the characters in the substring are identical.

Return the minimum length after the operations.

Intuition

To solve this problem, the goal is to minimize the length of the longest contiguous substring of identical characters after performing at most numOps flips. The approach involves using binary search over possible lengths of the longest identical substring and a function to check if it's feasible to achieve a given length with the allowed number of flips.

  1. Binary Search Approach: We utilize binary search to efficiently determine the smallest possible length of the longest substring where all characters are identical.

  2. Feasibility Check: For a given potential length, use a helper function to check whether it's possible to break all blocks of identical characters that exceed this length by performing the available flips. For substrings longer than this length, calculate how many operations are required to divide them into smaller acceptable lengths.

  3. Counting Flips: For the specific case where we check for a substring of length 1, we alternate the characters to minimize contiguous sequences, ensuring we're within the flip limit. For longer substrings, track contiguous sequences and calculate the necessary flips to fragment these blocks.

By structuring the solution in this way, the algorithm efficiently finds the optimal length of the longest permissible identical substring.

Learn more about Binary Search patterns.

Solution Approach

The solution employs a combination of binary search and a validation function to find the minimal possible length of the longest contiguous substring of identical characters after allowed flips.

  1. Binary Search:

    • We perform a binary search over possible lengths of the longest identical substring, ranging from 1 to n (the length of the input string s).
    • Use bisect_left from bisect module to implement the binary search efficiently.
  2. Validation Function (check):

    • Define a helper function check(m) to determine if it's feasible to limit the longest identical substring to a length m using at most numOps flips.
  3. Counting Operations:

    • For m = 1, alternate the characters in s (like '010101...' or '101010...') to see if the number of flips needed is within numOps.
    • For m > 1, traverse the string to count the length of contiguous identical substrings. If a block length exceeds m, calculate how many flips are required to make each segment have at most m identical characters.
    • Update the cnt (operation count) accordingly for each long contiguous block.
  4. Checking Feasibility:

    • The check function evaluates whether the computed cnt does not exceed numOps for each m. If feasible, the binary search narrows the search range.

This method is efficient because it leverages both binary search to quickly hone in on feasible solutions and a counting strategy to validate the intermediate results with precision.

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Example Walkthrough

Let's walk through the solution using a small example to understand the approach better. Consider the binary string s = "110100110" with numOps = 2.

  1. Initialization:

    • We need to find the minimum length of the longest substring of identical characters after up to 2 flips.
    • Use binary search on the length of the longest possible segment of identical characters from 1 to the length of s, which is 9.
  2. Binary Search:

    • Step 1: Set left to 1 and right to 9. Start with m = (1 + 9) // 2 = 5.

      • Check if we can ensure no block is longer than 5 with 2 flips.
      • A segment like 110 is already of length 2, which is fine. Segments 00 and 11 are also smaller than 5.
      • So, it's feasible, and we continue to see if smaller lengths are achievable by setting right = 5.
    • Step 2: Set m = (1 + 5) // 2 = 3.

      • Check if it's possible to limit blocks to length 3.
      • Here, we can split parts of 110100110 with flips:
        • Flip one 1 to 0 in the 110 or 011 substrings, and a 0 to 1 in the 00 or 11 substrings if needed, staying within 2 operations.
      • It's feasible with 2 flips, so we set right = 3.
    • Step 3: Set m = (1 + 3) // 2 = 2.

      • Check if we can reduce to blocks of length 2.
      • For 110100110, to reduce all segments to length 2, notice:
        • Substring 110, one flip changes to 10.
        • Substring 011, one flip changes to 01.
        • This uses exactly 2 flips.
      • Feasible, so set right = 2.
    • Step 4: Set m = (1 + 2) // 2 = 1.

      • Check if each block can be of length 1.
      • Alternating pattern requires too many flips (5 flips needed), exceeding numOps = 2.
      • Not feasible, so set left = 2.
  3. Outcome:

    • The binary search ends when left >= right.
    • The smallest feasible maximum length of identical character blocks is 2.

In this example, we used binary search to efficiently find that the minimized maximal block length of identical characters is 2 after performing at most two character flips.

Solution Implementation

1from bisect import bisect_left
2
3class Solution:
4    def minLength(self, s: str, numOps: int) -> int:
5        # Function to check if it's possible to transform s to a repeated pattern of length m
6        def check(m: int) -> bool:
7            cnt = 0  # Initialize the counter for operations
8            # Case when trying to create a pattern "01" or "10"
9            if m == 1:
10                pattern = "01"  # Define alternating pattern
11                cnt = sum(char == pattern[i % 2] for i, char in enumerate(s))  # Count how many characters need change to fit pattern
12                cnt = min(cnt, n - cnt)  # Minimize operations by choosing the easier pattern ("01" vs "10")
13            else:
14                consecutive_count = 0
15                # Iterate over the string to count unnecessary consecutive patterns
16                for i, char in enumerate(s):
17                    consecutive_count += 1  # Increment the consecutive count
18                    # If end of the string or next character is different, decide on how many groups can be formed
19                    if i == len(s) - 1 or char != s[i + 1]:
20                        cnt += consecutive_count // (m + 1)  # Add to operation count based on consecutive length
21                        consecutive_count = 0  # Reset consecutive count
22            return cnt <= numOps  # Check if the number of operations is within allowed limit
23
24        n = len(s)  # Length of the string
25        # Use binary search to find the minimum length of repeated pattern that can be formed
26        return bisect_left(range(n), True, lo=1, key=check)
27
1class Solution {
2    private char[] chars; // Array to hold character data
3    private int maxOperations; // Maximum allowable operations
4
5    public int minLength(String s, int numOps) {
6        this.maxOperations = numOps;
7        this.chars = s.toCharArray();
8        int left = 1, right = s.length(); // Initialize binary search boundaries
9        while (left < right) {
10            int mid = (left + right) >> 1; // Calculate the mid-point for binary search
11            if (check(mid)) {
12                right = mid; // Narrow down the search to the left half
13            } else {
14                left = mid + 1; // Narrow down the search to the right half
15            }
16        }
17        return left; // Return the minimum valid length
18    }
19
20    private boolean check(int m) {
21        int operationsCount = 0; // Count of required operations
22
23        if (m == 1) {
24            // Handle the special case when m = 1
25            char[] pattern = {'0', '1'};
26            for (int i = 0; i < chars.length; ++i) {
27                if (chars[i] == pattern[i & 1]) { // Check for alternating pattern
28                    ++operationsCount;
29                }
30            }
31            operationsCount = Math.min(operationsCount, chars.length - operationsCount);
32        } else {
33            int segmentLength = 0;
34            for (int i = 0; i < chars.length; ++i) {
35                ++segmentLength;
36                // Check for the end of the segment or a change in character
37                if (i == chars.length - 1 || chars[i] != chars[i + 1]) {
38                    operationsCount += segmentLength / (m + 1); // Calculate operations needed for the segment
39                    segmentLength = 0; // Reset segment length
40                }
41            }
42        }
43
44        return operationsCount <= maxOperations; // Evaluate if current m is feasible
45    }
46}
47
1#include <string>
2#include <algorithm>
3using namespace std;
4
5class Solution {
6public:
7    int minLength(string s, int numOps) {
8        int n = s.size(); // Get the size of the string
9        auto canAchieve = [&](int m) {
10            int cnt = 0; // Initialize a counter for operations needed
11
12            // Case when m equals 1
13            if (m == 1) {
14                string expectedPattern = "01"; // Alternating pattern
15                for (int i = 0; i < n; ++i) {
16                    // Check if current character matches the alternating pattern
17                    if (s[i] == expectedPattern[i % 2]) {
18                        ++cnt; // Increment count if matches
19                    }
20                }
21                cnt = min(cnt, n - cnt); // Get minimum number of operations required
22            } else {
23                int groupSize = 0; // Variable to track group size
24                for (int i = 0; i < n; ++i) {
25                    ++groupSize; // Increment group size
26                    // Check if it's the end of a group
27                    if (i == n - 1 || s[i] != s[i + 1]) {
28                        // Calculate the number of operations needed for this group
29                        cnt += groupSize / (m + 1);
30                        groupSize = 0; // Reset the group size for the next group
31                    }
32                }
33            }
34            return cnt <= numOps; // Check if we can achieve this with the given number of operations
35        };
36
37        int left = 1, right = n;
38        while (left < right) {
39            int mid = (left + right) >> 1; // Find the midpoint
40            // If the current mid can be achieved, search in the left half
41            if (canAchieve(mid)) {
42                right = mid;
43            } else {
44                left = mid + 1; // Else search in the right half
45            }
46        }
47        return left; // Return the minimum length found
48    }
49};
50
1// Function to determine the minimum possible length of string after performing operations
2function minLength(s: string, numOps: number): number {
3    const n = s.length;
4
5    // Inner function to check if a given length `m` is achievable with the allowed number of operations
6    const check = (m: number): boolean => {
7        let cnt = 0; // Initialize count of operations performed
8
9        if (m === 1) {
10            const alterPattern = '01'; // Alternating pattern for comparison
11            for (let i = 0; i < n; ++i) {
12                if (s[i] === alterPattern[i % 2]) {
13                    // Count the mismatches with alternating pattern
14                    ++cnt;
15                }
16            }
17            // Take the minimum of mismatches or matches to calculate operation count
18            cnt = Math.min(cnt, n - cnt);
19        } else {
20            let seqLength = 0; // Length of the current sequence of identical characters
21            for (let i = 0; i < n; ++i) {
22                ++seqLength;
23                if (i === n - 1 || s[i] !== s[i + 1]) {
24                    // Calculate the number of operations needed for this sequence
25                    cnt += Math.floor(seqLength / (m + 1));
26                    seqLength = 0; // Reset the sequence length for the next group
27                }
28            }
29        }
30        // Returns true if the required number of operations is less than or equal to allowed operations
31        return cnt <= numOps;
32    };
33
34    let [left, right] = [1, n]; // Initialize binary search boundaries
35
36    // Binary search to find the minimum length `m`
37    while (left < right) {
38        const mid = (left + right) >> 1; // Middle point
39
40        if (check(mid)) {
41            right = mid; // Try for a smaller length
42        } else {
43            left = mid + 1; // Increase the minimum possible length
44        }
45    }
46    return left; // Return the minimal length found
47}
48

Time and Space Complexity

The time complexity of the provided code is determined primarily by the check function and the use of bisect_left from Python's bisect module.

  1. The check function involves iterating over the string s, which has a length n. Within this loop, for each character, there is a constant amount of work. Therefore, the time complexity of each call to check is O(n).

  2. The bisect_left function is used with the key parameter as check, and it performs a binary search over a range from 1 to n. This involves roughly O(log n) calls to the check function.

Combining these, the overall time complexity is O(n log n).

The space complexity of the solution is O(1) since no additional data structures are used that grow with input size; the storage required does not depend on the size of n.

Thus, the complexities are:

  • Time Complexity: O(n log n)
  • Space Complexity: O(1)

Learn more about how to find time and space complexity quickly.


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