1D Dynamic Programming · 12 min · free with an account

Coin Change Minimum: When to Use It

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Recognise the problem shape before choosing Coin Change Minimum.

Core idea

Choose a pattern from the input guarantees and required operations, not from surface wording.

Start with the idea

A problem is a good match for Coin Change Minimum when it asks for minimum unbounded composition and denomination planning. Here the concrete task is: Return the fewest coins needed for amount 11. The visible input coins=[1,2,5] is an example, not a definition of the whole problem. Ask what operations the solution must support and what the input guarantees. Choose a recurrence when subproblems overlap. Some patterns summarize a frontier instead of a full DP table; either representation needs an invariant, and any greedy shortcut needs proof. The right choice follows from those guarantees, not from a familiar noun in the prompt.

The decisive invariant is: Let each amount state choose one coin plus the best smaller reachable amount. Try to say why it applies to the task without quoting the implementation. Which fact becomes known after one update, and which possible answers remain? A one-dimensional DP state summarizes a prefix or a single parameter. Define its meaning before writing a recurrence. If that claim cannot be maintained on a legal input, the pattern is wrong or one of its preconditions is missing. A counterexample is more useful than a vague warning: change one property of the sample, trace the first two steps, and identify the exact claim that fails.

Compare costs before committing. The advertised bound is O(amount * coins) time · O(amount) space; identify the operation responsible for it and the extra memory it needs.

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