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Core
Dynamic Programming
Dynamic programming solves problems that have overlapping subproblems and optimal substructure by defining a state, writing a recurrence, and caching results so each subproblem is computed once. The skill is the framework (state, recurrence, base case, order of evaluation), not memorizing tricks. Applied-AI interviews probe it because it screens for whether you can turn a fuzzy optimization into a precise recurrence rather than recognizing a pattern you saw before.
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