Get Min Operations
Reported by candidates from Citadel's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
Citadel hit candidates with this in July 2024, and it's a pattern problem that punishes brute force. You're asked to find the minimum number of operations to transform or reduce some value, usually by applying a fixed set of allowed moves. The trick: this screams dynamic programming or BFS, not greedy. Most candidates waste 10 minutes on a greedy approach that fails on edge cases. StealthCoder catches that instinct and feeds you the DP recurrence or graph search setup you need when you blank under pressure.
Pattern and pitfall
Get Min Operations almost always boils down to finding the shortest path in a state space. Each operation moves you from one state to another, and you want the minimum steps to reach a target. The common pitfall: thinking you can greedily pick the 'best' operation each time. Wrong. You need either BFS to explore states level-by-level, or memoized recursion to cache subproblems and avoid recomputation. The operations themselves are usually simple (subtract 1, divide by 2, etc.), but the graph they create is what matters. Citadel expects you to recognize this as a shortest-path problem, not an optimization problem. If you freeze on the logic, StealthCoder will show you the state-transition pattern and save you from timeout or TLE.
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Get Min Operations FAQ
Is this actually a DP problem or a graph search problem?+
Both. BFS is cleaner for shortest path if the graph is small. DP (memoization) is safer if you have recursive state transitions and need to avoid revisiting states. Citadel likely expects BFS or memoized recursion. Test your approach on small cases first.
What's the most common mistake candidates make?+
Trying to be greedy. Candidates assume 'always pick the biggest reduction' or 'always divide first' works. It doesn't. The problem often has tricky cases where a smaller operation chains into fewer total moves. Always think shortest path, not locally optimal.
How do I code this fast under time pressure?+
Write BFS with a queue and a visited set first. It's mechanical: explore neighbors, pop from queue, mark visited, check if target reached. If you get stuck, a memoized DFS with @cache is backup. Both run in similar time, BFS is easier to debug live.
What's the complexity I should target?+
Usually O(n) or O(n log n) time, where n is the range of states you explore. Space is O(n) for the queue or memo table. If you're seeing exponential behavior, you're not memoizing or pruning correctly.
Will this problem still have edge cases that break my first attempt?+
Yes. Test with n=1, n=large prime, and cases where two operations give the same cost but different paths. Citadel likes tricky boundaries. Have a few test cases ready before submitting.