Einsatz von dynamischer Programmierung
Wann ist es sinnvoll, dynamische Programmierung einzusetzen?
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Minimale Anzahl an Münzen
Gegeben sei ein Betrag n und eine Liste von Münzen coins. Implementieren Sie eine naive rekursive Funktion minCoins(n: int, coins: list[int]) -> int, die die minimale Anzahl an Münzen zurückgibt, die benötigt wird, um den Betrag n zu erreichen.
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Minimale Anzahl an Münzen mit dynamischer Programmierung
Stellen Sie die Funktion minCoins(n: int, coins: list[int]) -> int so um, dass sie dynamische Programmierung einsetzt.
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