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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