← Akuna Capital Interview Insights
Use a sliding window with two pointers to maintain a window containing at most k distinct characters. Expand the right pointer to include new characters, and when the distinct count exceeds k, shrink the window from the left until it's valid again. Track the maximum length and earliest start index.
Pro tip: Clarify edge cases upfront (e.g., k=0, empty string, k >= distinct characters) and discuss how you'd handle them. Also, mention that the earliest start index is naturally maintained by only updating when a strictly longer window is found.
Confirm the definition of 'substring' (contiguous), what to return if no such substring exists (e.g., empty string), and handle edge cases like k=0 or empty input.
Explain that a sliding window with two pointers (left and right) efficiently maintains a window with at most k distinct characters in O(n) time.
Use a hash map to count frequencies of characters in the current window. When the number of distinct characters exceeds k, move the left pointer and update the map until the window is valid again.
Whenever the window is valid, compare its length to the current maximum. If it's strictly greater, update the max length and record the start index (left pointer).
After traversing the string, return the earliest start index and max length. Explain that each character is processed at most twice, giving O(n) time and O(k) space.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem constraints and edge cases, then propose an algorithm that processes each node's incident edges to maintain the top-k positive weights using a min-heap of size k. Analyze the time complexity to ensure O(m log k) and discuss potential optimizations or trade-offs.
Pro tip: Emphasize that you would confirm whether k is fixed across all nodes and whether the graph is large enough to require streaming or memory-efficient processing, showing attention to practical constraints.
Ask about graph size, whether k is constant, if negative weights are ignored, and what to return if a node has fewer than k positive edges.
For each node, iterate through its incident edges, ignore non-positive weights, and maintain a min-heap of size k to keep the largest k weights.
Show that each edge is processed once per endpoint, and heap operations take O(log k), leading to O(m log k) total time.
Explain how to build the adjacency list, handle isolated nodes, and efficiently compute sums from the heap.
Mention alternatives like sorting all incident edges (O(d log d)) and argue why the heap approach is better for large degrees.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem and confirm that the count of pairs (i, j) with i < j, A[i]=1, A[j]=0 equals the minimum adjacent swaps to move all 1s to the right. Then, present a single-pass O(n) time, O(1) space algorithm: iterate through the array, maintain a count of ones seen so far, and for each zero encountered, add the number of ones seen to a running total. Finally, discuss why this works and analyze complexity.
Pro tip: Emphasize that the algorithm uses constant extra space by only keeping two integer variables, which is crucial for large datasets. Also, mention that the same logic can be applied to move all 0s to the left by swapping the roles of 0 and 1.
Restate the problem in your own words and confirm that the count of pairs (i, j) with i < j, A[i]=1, A[j]=0 is indeed the minimum number of adjacent swaps to move all 1s to the right. Ask if there are any constraints or edge cases to consider.
Explain the single-pass approach: initialize ones = 0 and swaps = 0. Iterate through the array; if the current element is 1, increment ones; if it is 0, add ones to swaps. This counts the number of inversions (1 before 0).
State that the algorithm runs in O(n) time because it makes a single pass, and uses O(1) extra space since only two integer variables are used regardless of input size.
Walk through a small example, such as [1,0,1,0], to demonstrate the algorithm. Show how the counts update and verify the result matches the expected number of swaps.
Mention edge cases like all 1s, all 0s, or empty array. Optionally, discuss how to modify the algorithm to move all 0s to the left or to handle other similar problems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.