← Bytedance Interview Insights
The 1D version clicked pretty fast once I thought about maintaining a window with a count of zeros inside it.
Start by clarifying the problem and constraints, then present the sliding window solution for the 1D case with O(n) time and O(1) space. For the 2D follow-up, explain how to reduce it to the 1D problem by fixing column boundaries and applying the sliding window on row sums, achieving O(m^2 * n) time. Discuss trade-offs and edge cases.
Pro tip: Explicitly state that the 1D problem is equivalent to finding the longest subarray with at most k zeros, and for 2D, mention that the optimal solution depends on matrix dimensions; if rows are much larger than columns, transpose the matrix to optimize complexity.
Ask about input size, whether k can be larger than the number of zeros, and if the matrix is binary. Confirm that flips are only allowed on zeros and that the subarray/submatrix must be contiguous.
Use two pointers (left and right) to maintain a window with at most k zeros. Expand right, count zeros, and when zeros exceed k, move left until zeros <= k. Track the maximum window length.
Iterate over all pairs of left and right columns. For each pair, compute an array where each element is the sum of that row segment (treating 1s as 1 and 0s as 0). Then apply the 1D sliding window on this array to find the maximum number of 1s (or equivalently, minimum zeros) with at most k flips.
State that the 1D solution is O(n) time and O(1) space. The 2D solution is O(m^2 * n) time and O(n) space, where m is the number of rows and n is the number of columns. Mention that transposing the matrix can reduce time if m > n.
Consider cases like all zeros, all ones, k=0, k >= total zeros, empty input, and single row/column. Walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
You know it's coming and you still have to code it.
Start by clarifying the requirements and constraints, then propose a hash map combined with a doubly linked list to achieve O(1) operations. Walk through the design, implement the core methods, and analyze time and space complexity.
Pro tip: Mention that you would use dummy head and tail nodes to simplify edge cases in the linked list, and discuss thread-safety considerations if the cache might be accessed concurrently.
Ask about cache capacity, eviction policy, and whether operations need to be thread-safe. Confirm that get and put must both be O(1) average time.
Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains recency order with O(1) insertion and deletion.
Detail how get moves the accessed node to the front, and put inserts or updates a node, evicting the least recently used (tail) when capacity is exceeded.
Write clean code for the LRUCache class, including helper methods for adding to front and removing nodes. Use dummy head and tail to avoid null checks.
State that both operations are O(1) time and O(capacity) space. Discuss edge cases like capacity 1, updating existing keys, and handling null values.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.