Started with the O(N^3) brute force just to show I wasn't going to skip steps, then moved to expand-around-center which got me to O(N^2) with constant space.
Start by clarifying the problem and constraints, then present a progression of solutions: brute force, dynamic programming, expand around center, and Manacher's algorithm. For each, explain time and space complexity and trade-offs, and finally discuss which approach is best for the given context (e.g., ML engineering at Waymo).
Pro tip: Relate the problem to real-world ML scenarios, such as processing sensor data or text sequences, and emphasize the importance of choosing the right algorithm based on constraints like input size and latency requirements.
Ask about input size, character set, and whether the palindrome needs to be contiguous. This helps determine the appropriate algorithm.
Explain checking all substrings and verifying palindrome, with O(n^3) time. Mention it's simple but inefficient for large inputs.
Describe using a DP table to store palindrome status of substrings, reducing time to O(n^2) and space O(n^2). Discuss trade-off of space.
Detail expanding from each character and between characters, achieving O(n^2) time and O(1) space. Highlight it's often preferred for its simplicity and low space.
Mention linear time O(n) solution, but note its complexity and that it's rarely required unless performance is critical. Compare trade-offs with previous methods.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.