What is the optimal time complexity of solving Design Add And Search Words?

The optimized solution achieves a time complexity of O(n) by using sorting, mathematical shortcuts, or hash table lookups.

What is the auxiliary space complexity for Design Add And Search Words?

The space complexity is O(1) since we only use a minimal/constant amount of extra memory for tracking variables (or auxiliary space if dynamic structures are allocated).

What are the typical edge cases we must handle in Design Add And Search Words?

Key edge cases include empty collections, negative or large bounds causing integer overflow, arrays with only one element, or inputs where target criteria are not met.

How can we optimize the brute force approach for Design Add And Search Words?

Brute force methods often inspect all possible combinations. We optimize this by sorting the input list to enable binary search, or tracking processed items in a set to avoid redundant nested loop lookups.

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Design Add And Search Words

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Problem Statement

Easy

Design a data structure that supports adding new words and finding if a string matches any previously added string.

Implement the WordDictionary class:

- WordDictionary() Initializes the object.

- addWord(word: str) Adds word to the data structure, it can be matched later.

- search(word: str) -> bool Returns True if there is any string in the data structure that matches word or False otherwise. word may contain dots '.' where dots can be matched with any letter.

Input is a list of operations and arguments. Implement a function wordDictionary(operations: list, arguments: list) -> list that returns a list of results (None for constructor/addWord, bool for search).

Constraints

•1 <= len(word) <= 25
•word in addWord consists of lowercase English letters
•word in search consists of '.' or lowercase English letters
•At most 10^4 calls will be made to addWord and search

Examples

Example 1

Input

operations = ["WordDictionary", "addWord", "addWord", "addWord", "search", "search", "search", "search"], arguments = [[], ["bad"], ["dad"], ["mad"], ["pad"], ["bad"], [".ad"], ["b.."]]

Output

[None, None, None, None, False, True, True, True]

Explanation

Initialize. Add "bad", "dad", "mad". search("pad") -> False. search("bad") -> True. search(".ad") -> True. search("b..") -> True.

Need a Hint?

Analyze the input constraints. Try sorting first (O(n log n)) or using a hash map/set to track seen elements in O(n) time.

Edge Cases to Watch

Empty list or null input variables
Single item lists/arrays
Extremely large input bounds causing integer or stack overflow

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