Coupon Code Validator - Solution & Explanation

Problem Overview: You receive a list of coupon codes and need to determine which ones are valid based on a set of rules such as formatting constraints, character checks, and duplicate detection. The goal is to process each code efficiently and return only the coupons that satisfy the validation rules.

Approach 1: Basic Simulation (O(n * k), O(1) space)

The most direct solution iterates through every coupon string and validates it character by character. You check conditions like allowed characters, length constraints, and formatting rules using simple loops. This approach treats each coupon independently and uses conditional checks to determine whether the code should be accepted or rejected. Time complexity is O(n * k) where n is the number of coupons and k is the length of each coupon string, while extra space remains O(1) because validation happens in-place without additional data structures. This works well when the rules are simple and the dataset is small.

Approach 2: Hash Table for Duplicate Detection (O(n * k), O(n) space)

If coupon validity requires detecting duplicates or repeated codes, a hash table improves efficiency. As you iterate through the list, normalize the coupon (for example by converting to lowercase or trimming whitespace) and store it in a hash set. A constant-time lookup quickly tells you if the code already appeared earlier. The validation still scans each string once, keeping time complexity at O(n * k), but space increases to O(n) for storing previously seen coupons. This pattern is common in problems involving uniqueness checks across arrays.

Approach 3: Sorting-Based Canonical Validation (O(n * k log k), O(n) space)

Some validation rules treat coupons with the same characters as equivalent regardless of order. In that case, convert each string into a canonical form by sorting its characters. Two coupons with identical sorted representations are effectively duplicates. This uses string manipulation and sorting. For each coupon you sort its characters in O(k log k), then store the canonical version in a hash set. Overall complexity becomes O(n * k log k) time and O(n) space. The benefit is simpler duplicate detection for permutation-based rules.

Recommended for interviews: Start by explaining the basic simulation since it mirrors the problem statement and demonstrates clear reasoning. Then improve it using a hash set to track duplicates or normalized forms of coupons. Interviewers usually expect the hash-table-based approach because it maintains linear processing of the input while ensuring efficient lookups.