Sliding window with a set, pretty textbook.
Use the sliding window technique with a hash map to track the last seen index of each character. Expand the right pointer, and when a duplicate is found, move the left pointer to the maximum of its current position and the last seen index + 1. Keep track of the maximum window length throughout.
Pro tip: Clarify whether the input is a string or an array, and discuss the trade-offs between using a fixed-size array (for ASCII) versus a hash map (for Unicode) to demonstrate attention to constraints and optimization.
Ask about the input type (string, array), character set (ASCII, Unicode), and whether the subarray must be contiguous. Confirm that we need the length, not the actual subarray.
Decide between a hash map (general) or an array (if character set is small and known) to store the last seen index of each element. Explain your choice based on constraints.
Initialize left and right pointers at 0, and max_length at 0. Iterate right from 0 to n-1. If the current element is in the map and its last seen index >= left, update left to last_seen + 1. Update the map with the current index. Update max_length.
State that time complexity is O(n) because each element is visited at most twice (by right and left pointers). Space complexity is O(min(n, m)) where m is the size of the character set.
Walk through a simple example (e.g., 'abcabcbb') and an edge case (empty input, all unique, all duplicates) to verify correctness and handle boundaries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Classic cycle detection on a directed graph.
Model the courses and prerequisites as a directed graph, then check for cycles using either Kahn's algorithm (BFS-based topological sort) or DFS with recursion stack. If a cycle exists, not all courses can be completed; otherwise, they can.
Pro tip: Clarify edge cases upfront (e.g., duplicate prerequisites, self-loops, disconnected graphs) and mention that the problem reduces to cycle detection in a directed graph. This shows you think about robustness and can save time during implementation.
Ask about input format (e.g., number of courses, list of prerequisite pairs), constraints (e.g., course labels, possible duplicates), and expected output (boolean). Confirm that prerequisites form a directed edge from prerequisite to course.
Represent courses as nodes and prerequisites as directed edges. Build an adjacency list and optionally an in-degree array for Kahn's algorithm.
Decide between Kahn's algorithm (BFS topological sort) or DFS with recursion stack. Explain the trade-offs: Kahn's is iterative and easy to reason about; DFS can be more concise but requires careful state tracking.
Write clean code for the chosen method, handling edge cases like empty input, no prerequisites, and disconnected components. Walk through a small example to verify correctness.
State time and space complexity: O(V + E) time and O(V + E) space, where V is number of courses and E is number of prerequisite pairs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Recognize this as a classic 'minimize the maximum' problem that can be solved with binary search on the answer. Define the search space between the maximum package weight and the total sum of all packages, then for each candidate capacity, simulate the shipping process to check if it's feasible within the given days. Return the smallest feasible capacity.
Pro tip: Always clarify edge cases upfront, such as when the number of days is less than the number of packages (impossible) or when a single package exceeds the capacity. Also, mention that the simulation can be optimized by greedily loading packages in order, which is optimal for this problem.
Restate the problem: given an array of package weights and a number of days, find the minimum ship capacity to ship all packages within that many days. Clarify that packages must be shipped in order and cannot be split.
Explain that the answer lies between the maximum single package weight (lower bound) and the sum of all weights (upper bound). Use binary search to efficiently find the minimum capacity.
Write a helper function that, given a capacity, simulates shipping by greedily adding packages to the current day's load until adding the next would exceed capacity, then incrementing the day count. Return whether the total days needed is <= the given days.
Perform binary search on the capacity range, updating the bounds based on the feasibility check. When the search converges, return the lower bound as the minimum capacity.
State that the time complexity is O(n log(sum - max)) and space is O(1). Discuss edge cases like days < number of packages (impossible) or days >= number of packages (capacity = max weight).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.