Clarify the problem constraints (e.g., array size, duplicates, whether the array is sorted) and then propose a binary search solution that finds the insertion point of the target. Compare the element at that point and its predecessor to determine the closest value, handling edge cases like target outside the array bounds.
Pro tip: Mention that the problem can be solved in O(log n) time using binary search, and explicitly discuss how to handle ties (e.g., when two elements are equally close) to show attention to detail. Also, consider if the array is empty or has one element.
Ask about array size, duplicates, sorted order, and what to return if multiple elements are equally close. Confirm if the array can be empty or contain one element.
Propose binary search to find the insertion point of the target in O(log n) time, rather than a linear scan. Explain that this leverages the sorted property.
Use binary search to find the index where the target would be inserted to keep the array sorted. This gives the closest candidates: the element at that index and the one before it.
Check if the insertion point is at the start or end of the array, and compare the absolute differences to select the closest. If differences are equal, decide based on problem requirements (e.g., return the smaller or larger).
State that time complexity is O(log n) and space is O(1). Walk through test cases: target present, target between two elements, target smaller than all, target larger than all, and empty array.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.