I recognized the coin change shape pretty quickly, which helped.
Recognize this as the unbounded knapsack/coin change problem where order doesn't matter and repetition is allowed. Use dynamic programming to determine if the target sum is reachable, then backtrack to construct a valid combination. Discuss time and space complexity and potential optimizations.
Pro tip: Clarify with the interviewer whether you need to return any valid combination or all combinations, and whether the order of experiences matters. This shows attention to detail and avoids over-engineering.
Ask about input constraints, whether experiences can be repeated, if order matters, and what to return if no combination exists. Confirm the expected output format.
Recognize this as a variation of the coin change problem (unbounded knapsack). Decide between DP, BFS, or recursive backtracking with memoization based on constraints.
Create a boolean DP array where dp[i] indicates if sum i is reachable. Iterate through sums and experiences to fill the array. For reconstruction, store the last experience used to reach each sum.
If dp[layover] is true, backtrack from layover using the stored choices to build the list of experiences. Return the list; otherwise return null or an empty list.
State time complexity O(n * m) where n is layover and m is number of experiences, and space O(n). Mention potential optimizations like using BFS for shortest combination or pruning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.