The basic cases are easy but the edge cases pile up fast.
Clarify the exact definition of a valid number (e.g., leading/trailing spaces, scientific notation, leading zeros) and then implement a deterministic finite automaton (DFA) that processes the string character by character. Alternatively, use a well-structured regex, but be prepared to explain its components and edge cases.
Pro tip: At Meta, interviewers value clean, maintainable code and thorough edge-case handling. Start by writing a clear state machine on the whiteboard, then translate it to code—this shows systematic thinking and reduces bugs.
Ask the interviewer about the exact definition of a valid number: Are leading/trailing spaces allowed? Is scientific notation (e.g., '1e10') considered valid? Are leading zeros allowed? This ensures you solve the right problem.
Decide between a regex and a state machine. A regex is concise but can be hard to explain; a state machine is more verbose but demonstrates algorithmic thinking and is easier to extend.
Define states such as start, sign, integer, dot, fraction, and accept. Map transitions for digits, signs, dots, and possibly exponent characters. Ensure you handle edge cases like a lone sign or multiple dots.
Write the code based on the state machine, then walk through test cases: valid numbers (e.g., '123', '-4.56'), invalid ones (e.g., '123abc', '--6'), and edge cases (e.g., '.', '-.', '1.2.3').
State that the time complexity is O(n) and space complexity is O(1) for the state machine approach, as it processes each character once with constant extra space.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.