I knew this one but still fumbled the justification for going back-to-front.
Use three pointers: one at the last valid element of the first array (m-1), one at the last element of the second array (n-1), and one at the last position of the first array's total capacity (m+n-1). Compare elements from the end and place the larger one at the write pointer, moving pointers backward. This avoids overwriting unprocessed elements and achieves O(m+n) time and O(1) space.
Pro tip: Clarify the problem constraints upfront (e.g., whether the arrays are truly in-place and if extra space is allowed) and mention edge cases like empty arrays or when one array is exhausted. This shows thoroughness and prevents misunderstandings.
Restate the problem to ensure understanding: merge two sorted arrays in-place, with the first array having enough trailing space. Confirm constraints like no auxiliary array and that arrays are sorted ascending.
Set three pointers: i = m-1 (last element of first array), j = n-1 (last element of second array), and k = m+n-1 (last position of merged array).
While i >= 0 and j >= 0, compare nums1[i] and nums2[j]. Place the larger at nums1[k], then decrement the corresponding pointer and k.
If j >= 0 after the loop, copy remaining elements from nums2 into nums1. If i >= 0, they are already in place.
State that time complexity is O(m+n) and space complexity is O(1), as no extra array is used.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Easier than I expected once you realize it's just flipping the comparison operator and reversing pointer direction.
Acknowledge that the core merge logic remains the same but the comparison direction flips: instead of picking the smaller element, you pick the larger one to build the merged array in descending order. Then discuss how to adapt the two-pointer technique, including edge cases and potential optimizations.
Pro tip: Mention that you can often avoid rewriting the merge by reversing both input arrays, merging as usual, and then reversing the result—this shows you think about code reuse and trade-offs. Also, clarify whether the output should be descending or ascending, as the question might imply a different requirement.
Confirm whether the merged array should be in descending order (matching inputs) or ascending. This determines the comparison direction.
If output is descending, compare the current elements and pick the larger one to append. If ascending, pick the smaller one, but note that inputs are descending so you may need to traverse from the end.
Use two pointers starting at the beginning (for descending output) or at the end (for ascending output). Explain how pointer movement changes accordingly.
Cover empty arrays, one array exhausted, duplicates, and stability. Confirm time and space complexity remain O(n+m) and O(n+m) or O(1) if in-place.
Suggest reversing inputs, merging, and reversing back as a way to reuse existing code, and discuss the trade-offs (extra O(n+m) time and space).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
No coding required, just discussion, but I blanked on structuring the answer cleanly.
First, clarify the problem context: what operation is needed on the two unsorted arrays (e.g., find common elements, merge, find pairs summing to a target). Then, present multiple approaches with their time/space complexity tradeoffs, such as sorting both arrays first, using a hash set, or brute force. Finally, recommend the most suitable approach based on constraints like memory limits, input size, and whether the arrays can be modified.
Pro tip: Always ask clarifying questions about the expected output and constraints before diving into solutions; this shows you think before coding and aligns with Amazon's Leadership Principles like Customer Obsession and Dive Deep.
Ask what operation is required on the two unsorted arrays (e.g., intersection, union, pair sum) and any constraints on time, space, or input size. This ensures you solve the right problem.
List viable strategies: brute force O(n*m), sorting both arrays O(n log n + m log m) then two-pointer, or using a hash set O(n+m) time with O(n) space. Mention if one array is much smaller, hash the smaller one.
Compare time and space complexities of each approach. Discuss when sorting is preferable (e.g., memory constrained, arrays can be modified) versus hashing (e.g., need O(n) time, extra space allowed).
Address duplicates, large inputs, streaming data, and whether the arrays fit in memory. Mention that sorting may be done in-place to save space, while hashing requires extra memory.
Choose the best approach based on the clarified constraints and explain why, showing awareness of real-world tradeoffs. For example, if memory is tight, sort; if speed is critical, hash.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.