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Each monster can only have two states. They are either alive or dead.
We can use bitmasks to represent every possible combination of alive and dead monsters.
Let dp[mask] represent the minimum number of days needed to reach the state mask.
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Bitmask DP efficiently represents all subsets of monsters and avoids recalculating results for the same state. Since the order of defeating monsters affects the total time, this method systematically explores all possibilities while caching results.
Problems involving bitmask dynamic programming and permutation optimization are common in top tech interviews. While this exact problem may vary, similar subset DP patterns frequently appear in FAANG-style interview questions.
The optimal approach uses dynamic programming with bitmasking. Each bitmask represents a subset of defeated monsters, and transitions simulate killing the next monster while updating the required time based on the current attack gain.
A DP array indexed by bitmask is the primary structure used in this problem. Bit operations help represent subsets, count defeated monsters, and transition to new states efficiently.