I used the standard Iterative Modulo and Division method for base conversion. This is the same mathematical process used to convert numbers to binary, octal, or hexadecimal manually.
I used a Breadth-First Search (BFS) approach. Since the problem asks for the minimum number of removals, BFS is ideal because it guarantees that the first time we find a valid string, it is at the shortest "distance" (fewest removals) from the original string.
I used a Linear Scan approach. Since the structure of each item is fixed (always [type, color, name]), we can map the ruleKey to a specific index and check every item against the ruleValue.
I used a Two Pointer simulation approach. This method mimics the manual "column addition" we learn in elementary school, processing digits from right to left and managing the carry.
I used a Set Lookup approach. By converting each row of the keyboard into a set of characters, we can efficiently check if every letter of a word belongs to the same set.
I used a Two Pointer (Greedy) approach. We traverse the target string t once, looking for characters from s in order.
I used a bit-by-bit simulation approach, similar to how we perform column addition manually. By iterating from the end of both strings to the beginning, we can compute the sum and carry for each position.
I used a bit-by-bit simulation approach, similar to how we perform column addition manually. By iterating from the end of both strings to the beginning, we can compute the sum and carry for each position.
I used a Monotonic Stack combined with a Hash Map. This allows us to find the next greater element for every number in nums2 in a single pass ($O(n)$), rather than using nested loops ($O(n^2)$).
I used a Hash Map (Frequency Counter) approach. Since string t is generated by shuffling string s and adding one more letter, the character counts will be identical except for exactly one character.