Is Wildcard Matching easy or hard?

Wildcard Matching is classified as a hard problem because it combines string parsing, edge-case handling, and algorithm optimization. Understanding the DP transition rules is manageable, but discovering the linear-time greedy strategy requires deeper insight.

How to solve Wildcard Matching in O(n)?

An O(m+n) solution uses two pointers and a greedy strategy. When a '*' appears in the pattern, store its position and the corresponding string index. If a mismatch happens later, backtrack to the stored star and expand the match by one character. This avoids building a DP table and keeps memory usage constant.

What is the best approach for Wildcard Matching?

The most widely taught solution is Dynamic Programming because it clearly models matching between prefixes of the string and pattern. However, the optimal solution uses a greedy two-pointer technique with backtracking to the last '*' position. That method runs in O(m+n) time and O(1) space, making it more efficient for large inputs.

What data structure is used in Wildcard Matching?

The Dynamic Programming approach uses a 2D boolean DP table to store match states between prefixes of the string and pattern. The greedy solution instead relies on pointer tracking and a few integer variables, avoiding additional data structures entirely.

What is the time complexity of Wildcard Matching?

The Dynamic Programming approach runs in O(m*n) time where m is the length of the string and n is the length of the pattern. The optimized greedy algorithm reduces this to O(m+n) by scanning both strings once while tracking the most recent '*' position.

Wildcard Matching Python or Java solution approach

Both Python and Java implementations usually follow either the DP table method or the greedy two-pointer algorithm. The DP version uses nested loops to fill a boolean matrix, while the greedy version uses pointer indices and star tracking variables to simulate pattern expansion.

Is Wildcard Matching asked at Google, Amazon, or Meta?

Wildcard-style pattern matching problems appear frequently in interviews at companies like Amazon, Google, and Meta. Variants test string processing, backtracking, and dynamic programming skills, making this problem a common hard-level interview question.

Description Solution Video 43:52

44. Wildcard Matching

Hard32.8% acceptance17 companies

Given an input string (s) and a pattern (p), implement wildcard pattern matching with support for '?' and '*' where:

'?' Matches any single character.
'*' Matches any sequence of characters (including the empty sequence).

The matching should cover the entire input string (not partial).

Example 1:

Input: s = "aa", p = "a"
Output: false
Explanation: "a" does not match the entire string "aa".

Example 2:

Input: s = "aa", p = "*"
Output: true
Explanation: '*' matches any sequence.

Example 3:

Input: s = "cb", p = "?a"
Output: false
Explanation: '?' matches 'c', but the second letter is 'a', which does not match 'b'.

Constraints:

0 <= s.length, p.length <= 2000
s contains only lowercase English letters.
p contains only lowercase English letters, '?' or '*'.

Asked at 17 companies

Twitter Cyntexa Snapchat Two Sigma Zoho Coupang Instacart Walmart Labs

Google Bloomberg

Infosys Amazon

+5 more

Microsoft Meta D. E. Shaw Atlassian Oracle

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44. Wildcard Matching

Asked at 17 companies

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