I went straight for recursion and it mostly worked, but splitting the arguments was where I fumbled.
Clarify the grammar and constraints, then implement a recursive descent parser that evaluates as it parses. Use a helper function that reads a function name, parses comma-separated arguments recursively, and applies the operation.
Pro tip: Mention that you can avoid building an AST by evaluating during parsing, which is more memory-efficient. Also, discuss handling edge cases like whitespace, negative numbers, and deeply nested inputs to show robustness.
Ask about the allowed operations, input format (spaces, negative numbers), and error handling. Confirm whether to evaluate on the fly or build an AST.
Write a simple grammar: expression = functionName '(' expression (',' expression)* ')' | number. This helps structure the parser.
Write a parser that reads a function name, then parses arguments recursively until the closing parenthesis. Evaluate each function call as soon as its arguments are parsed.
Parse integer literals (including negative signs) and skip whitespace between tokens. Ensure the parser correctly identifies the end of an argument.
Test with nested examples and edge cases. Analyze time complexity O(n) and space complexity O(d) for recursion depth d.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Modeled it as a directed graph and ran BFS from each node, tracking visited counts.
Model the bombs as nodes in a directed graph where an edge exists from bomb A to bomb B if A's blast radius covers B's position. The problem then reduces to finding the node with the maximum reachable set in this directed graph, which can be solved using DFS/BFS from each node or more efficiently with strongly connected components and condensation. Return the size of the largest reachable set.
Pro tip: Clarify whether the graph is directed (chain reaction only propagates outward from the initial blast) and mention that the naive O(n^2) graph construction is acceptable for typical constraints, but you can optimize with spatial indexing if needed. Also, note that if the graph has cycles, you must handle them to avoid infinite loops.
Confirm that bombs explode in a chain reaction: when a bomb explodes, it triggers all bombs within its blast radius, which then trigger others. Ask about input size to determine if O(n^2) is acceptable.
For each bomb i, check all other bombs j and add a directed edge i -> j if the distance between them is <= radius[i]. This takes O(n^2) time.
For each bomb, perform DFS or BFS to count how many bombs are reachable. Keep track of the maximum count. Use visited array to avoid revisiting nodes within a single traversal.
If n is large, consider using Tarjan's algorithm to find strongly connected components, condense the graph, and then compute reachable sets on the DAG using DP or topological order.
After checking all starting bombs, return the maximum number of bombs that can be detonated in a chain reaction.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.