Sliding window or just a linear scan works fine here.
Clarify the input format (e.g., a 2D boolean matrix of days × services) and define a healthy day as one where all services pass. Then iterate through the days, maintaining a running streak count that resets to zero on any unhealthy day, and track the maximum streak seen.
Pro tip: Mention that you can optimize by short-circuiting the check for each day (stop at the first failed service) and that the solution is O(D × S) time and O(1) extra space, which is optimal since you must inspect every cell in the worst case.
Ask about the data representation (e.g., list of lists, CSV) and edge cases like empty input or all healthy days. Confirm that a day is healthy only if every service passes.
Explain that you will scan days sequentially, compute whether each day is healthy, and update the current and maximum streak lengths accordingly.
Use a small example (e.g., 3 services, 5 days) to demonstrate how the streak resets on an unhealthy day and how the maximum is tracked.
State that the time complexity is O(D × S) where D is the number of days and S is the number of services, and space complexity is O(1) beyond the input.
Mention handling of empty input, all unhealthy days, and the possibility of short-circuiting the per-day check to avoid unnecessary evaluations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem and edge cases, then propose an efficient sliding window approach that expands and contracts to find the shortest substring containing all distinct characters. Explain the algorithm step-by-step, analyze its time and space complexity, and optionally discuss alternative methods like binary search with sliding window.
Pro tip: Demonstrate strong communication by walking through a concrete example (e.g., 'aabcbcdbca') to illustrate how the window adjusts, and mention that the sliding window approach is optimal because it processes each character at most twice, achieving O(n) time.
Confirm that the substring must be contiguous and contain every distinct character from the original string at least once. Discuss edge cases: empty string, single character, all characters distinct, and repeated characters.
Use two pointers (left and right) to represent a window. Expand right to include characters until all distinct characters are covered, then shrink left to minimize the window while maintaining coverage.
Maintain a frequency map (or array) for characters in the current window and a counter for how many distinct characters are fully covered. Update the minimum length whenever the window is valid.
Explain that each character is added and removed at most once, giving O(n) time and O(k) space where k is the number of distinct characters. Argue correctness by invariant: the window always contains all distinct characters when valid.
Mention that a brute-force approach is O(n^2) and inefficient. Optionally, describe a binary search on substring length combined with a sliding window check, which also runs in O(n log n) but is less optimal than the two-pointer method.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem: we have a list of product IDs (with possible duplicates) and must delete exactly m individual occurrences to minimize the number of distinct IDs remaining. The optimal strategy is to delete all occurrences of the least frequent IDs first, because removing a distinct ID entirely reduces the distinct count by 1 at the lowest cost. Sort the frequency counts ascending, then greedily delete entire groups while the cumulative frequency does not exceed m; if m remains after removing some groups, the remaining distinct count is the answer.
Pro tip: Always confirm edge cases with the interviewer: what if m is larger than the total number of occurrences? What if m equals the total? Also, mention that the greedy choice is optimal because deleting a less frequent ID always costs fewer deletions per distinct ID removed.
Restate the problem to ensure understanding: we have a list of product IDs (with duplicates) and an integer m. We must delete exactly m individual occurrences to minimize the number of distinct IDs remaining. Confirm that we can delete any occurrences, not necessarily entire groups.
Compute the frequency of each distinct product ID. This gives a list of counts representing how many occurrences each ID has.
Sort the frequency counts in ascending order. This allows us to consider removing the least frequent IDs first, which is optimal for minimizing distinct count.
Iterate through the sorted frequencies. For each frequency f, if m >= f, subtract f from m and reduce the distinct count by 1 (i.e., remove that ID entirely). If m < f, stop; we cannot remove another entire ID without exceeding m deletions.
After the loop, the number of distinct IDs left is the answer. If m becomes 0 exactly after removing some groups, the remaining distinct count is the answer. If m is still positive but less than the next frequency, we cannot reduce the distinct count further.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
A string can form a palindrome if at most one character has an odd frequency.
First, clarify that two strings can form a palindrome if and only if their combined character counts have at most one odd frequency. Then, represent each string by a bitmask of odd-count characters and count pairs whose masks differ by at most one bit (including identical masks). Use a hash map to count mask frequencies and compute the number of valid pairs efficiently.
Pro tip: Mention that the bitmask approach works because there are only 26 lowercase letters, so the mask fits in an integer; this shows you consider constraints and optimize accordingly. Also, explicitly handle the case where masks are identical, as that is a common oversight.
Explain that a string can be rearranged into a palindrome if and only if at most one character has an odd count. For two strings combined, this means the XOR of their odd-count masks has at most one bit set.
For each string, compute a 26-bit integer where each bit represents the parity (odd/even) of the count of a letter. This mask captures the essential information for palindrome formation.
Use a hash map to store the frequency of each mask. For each mask, count pairs with identical masks and with masks that differ by exactly one bit (i.e., mask XOR (1<<k) for each k). Sum these counts.
State that the time complexity is O(n * 26) and space O(n). Discuss edge cases like empty strings, strings with all even counts, and large input sizes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.