This one took me a minute to even figure out where to start.
Clarify requirements and edge cases, then propose a two-stack (operands and operators) iterative solution with a precedence map. Walk through the algorithm on a small example, then discuss complexity and potential optimizations like a recursive descent parser.
Pro tip: Explicitly handle division truncation toward zero and negative intermediate results, as many candidates overlook this. Also, mention that a recursive descent parser is more extensible for adding functions or variables, showing foresight.
Ask about input constraints (e.g., integer overflow, whitespace handling, unary operators) and confirm division truncation behavior. This ensures you build the right solution and demonstrates thoroughness.
Decide between two-stack (shunting-yard) and recursive descent. For this problem, two-stack is straightforward; recursive descent is more extensible but requires more code. Explain your choice.
For two-stack: iterate through characters, push numbers onto operand stack, and for operators, pop and apply while top-of-stack has higher or equal precedence. Handle parentheses by pushing '(' and evaluating until ')'. At the end, evaluate remaining operators.
Trace the algorithm on a sample expression like '3+2*2' to show how precedence is handled. This validates your logic and helps the interviewer follow.
State O(n) time and O(n) space. Mention potential optimizations like using a single stack with operator precedence or a recursive descent parser for better extensibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Easier than the first one once I remembered Union-Find.
Start by clarifying the problem constraints (e.g., graph size, node ID range) and then outline the Union-Find (Disjoint Set Union) data structure with path compression and union by rank/size. Walk through the algorithm step-by-step: initialize each node as its own parent, union nodes for each edge, then group nodes by their root to form components. Finally, sort each component's node list and return the list of components.
Pro tip: Mention that Union-Find is particularly efficient for dynamic connectivity and can be extended to handle large-scale graphs, which is relevant for ML applications like clustering or social network analysis at Snapchat. Also, discuss the time complexity: nearly O(E α(V)) with optimizations, which is effectively linear.
Ask about graph representation (edge list, adjacency list), node ID range, whether the graph is connected, and expected output format. Confirm if node IDs are integers and if sorting is required.
Explain the parent array, rank/size array, and the find and union operations with path compression and union by rank/size. Emphasize how these optimizations keep the tree shallow.
Iterate through all edges, perform union for each edge's endpoints. After processing, use a hash map to group nodes by their root (find(node)).
For each group, sort the node IDs and collect them into a list. Return the list of sorted components, ensuring no duplicates and correct ordering.
Discuss time complexity O(E α(V)) and space O(V). Handle edge cases: empty graph, single node, disconnected nodes, and nodes with no edges.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.