#1023

Medium

auto_awesomeTwo-pointer scanning with invariant tracking

LeetCode Problem Workspace

Camelcase Matching

Camelcase Matching is a medium difficulty problem where you match queries to a given pattern by inserting lowercase letters.

array two pointers string trie string matching

Problem Statement

You are given an array of strings queries and a string pattern. For each query in queries, return a boolean indicating whether the query can be transformed into the pattern by inserting lowercase letters into it. Insertion can happen at any position, or no insertion may be required at all. Your task is to efficiently check these matches.

For example, a query "FooBar" matches the pattern "FB" because it can be transformed by inserting lowercase letters: "F" + "oo" + "B" + "ar". The challenge is to handle multiple queries and determine if they satisfy the pattern constraints using a pattern-based solution.

Examples

Example 1

Input: queries = ["FooBar","FooBarTest","FootBall","FrameBuffer","ForceFeedBack"], pattern = "FB"

Output: [true,false,true,true,false]

"FooBar" can be generated like this "F" + "oo" + "B" + "ar". "FootBall" can be generated like this "F" + "oot" + "B" + "all". "FrameBuffer" can be generated like this "F" + "rame" + "B" + "uffer".

Example 2

Input: queries = ["FooBar","FooBarTest","FootBall","FrameBuffer","ForceFeedBack"], pattern = "FoBa"

Output: [true,false,true,false,false]

"FooBar" can be generated like this "Fo" + "o" + "Ba" + "r". "FootBall" can be generated like this "Fo" + "ot" + "Ba" + "ll".

Example 3

Input: queries = ["FooBar","FooBarTest","FootBall","FrameBuffer","ForceFeedBack"], pattern = "FoBaT"

Output: [false,true,false,false,false]

"FooBarTest" can be generated like this "Fo" + "o" + "Ba" + "r" + "T" + "est".

Constraints

1 <= pattern.length, queries.length <= 100
1 <= queries[i].length <= 100
queries[i] and pattern consist of English letters.

Solution Approach

Two-Pointer Scanning

The solution employs a two-pointer approach where one pointer traverses the pattern and another pointer scans through each query string. As you match uppercase characters in the query with the corresponding characters in the pattern, the pointers move together while skipping over the lowercase characters in the query.

Invariant Tracking

Invariant tracking is crucial in ensuring that each query matches the pattern in sequence. As the two pointers move, the relative position of uppercase characters in the query must align with the uppercase characters in the pattern. This prevents mismatch errors and ensures correct validation.

Efficient Matching

By utilizing two-pointer scanning and invariant tracking, this approach checks each query's validity in linear time relative to the size of the query and pattern. This is efficient for handling the constraints of the problem, especially with multiple queries and varying pattern lengths.

Complexity Analysis

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

The time complexity of the approach is O(n * m), where n is the number of queries and m is the average length of a query. Each query is processed linearly, and the number of operations grows with the number of queries and the length of the query strings. Space complexity depends on storing the queries and pattern, but it can be considered O(1) additional space as no auxiliary structures are used outside the input strings.

What Interviewers Usually Probe

The candidate effectively demonstrates two-pointer scanning and how it is used to match the query and pattern.
The candidate explains invariant tracking as a core concept for ensuring that the pattern's case constraints are respected during matching.
The candidate considers time and space efficiency when processing multiple queries, showing understanding of problem scale.

Common Pitfalls or Variants

Common pitfalls

Failing to properly match uppercase characters, causing incorrect results when letters are skipped.
Not accounting for the case sensitivity of the pattern, leading to false negatives when lowercase letters are inserted improperly.
Mismanaging the two-pointer movement, resulting in unnecessary comparisons and inefficient matching logic.

Follow-up variants

Matching multiple queries with different pattern lengths, requiring a dynamic approach for varying input sizes.
Supporting more complex patterns that may contain both uppercase and lowercase letters interspersed within the pattern.
Incorporating additional constraints, such as ensuring that inserted characters are non-repetitive or constrained to certain ranges.

FAQ

What is Camelcase Matching?

Camelcase Matching is a problem where you match queries to a given pattern using a two-pointer approach, ensuring the correct case-sensitive alignment of letters.

How does the two-pointer technique work in Camelcase Matching?

The two-pointer technique uses one pointer to traverse the pattern and another to scan through each query, checking for alignment of uppercase characters and skipping lowercase ones.

What should I be cautious about while solving Camelcase Matching?

Be mindful of case sensitivity and the need to handle multiple queries efficiently. Incorrect pointer movement or case handling may lead to false negatives.

What is the time complexity of the Camelcase Matching solution?

The time complexity is O(n * m), where n is the number of queries and m is the average length of the queries. This is due to the need to check each query against the pattern.

What are the common errors in Camelcase Matching?

Common errors include misaligning uppercase characters, not properly skipping lowercase letters, and inefficient pointer management, leading to slower solutions.

terminal

Solution

Solution 1: Two Pointers

We can traverse every string in `queries` and check whether it matches `pattern` or not. If it matches, we add `true` to the answer array, otherwise we add `false`.

class Solution:
    def camelMatch(self, queries: List[str], pattern: str) -> List[bool]:
        def check(s, t):
            m, n = len(s), len(t)
            i = j = 0
            while j < n:
                while i < m and s[i] != t[j] and s[i].islower():
                    i += 1
                if i == m or s[i] != t[j]:
                    return False
                i, j = i + 1, j + 1
            while i < m and s[i].islower():
                i += 1
            return i == m

        return [check(q, pattern) for q in queries]

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