Get Minimum Moves
Reported by candidates from Unknown Company's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
You're facing a minimization problem with limited context, which is exactly what makes these tricky on timed OAs. "Get Minimum Moves" is a classic framing that could hide several patterns. May 2025 reports show candidates blanking on whether it's a BFS shortest path, a greedy simulation, or a dynamic programming state search. The good news: most "minimum moves" problems follow one of three playbooks. StealthCoder can surface the pattern in seconds if you freeze mid-attempt.
Pattern and pitfall
Without the full problem text, the likely patterns are BFS (if it's grid-based or state-space), greedy (if you're optimizing a sequence), or DP (if there are overlapping subproblems). The common pitfall is assuming one move equals one step, when sometimes moves have different costs or effects. The trick is often that you can't just simulate forward; you need to think backward from the goal or collapse states to avoid redundant work. If it's a graph problem, Dijkstra or 0-1 BFS might be needed instead of naive BFS. StealthCoder will read the constraint bounds and alert you to which approach fits the input size.
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Get Minimum Moves FAQ
Is this a graph shortest-path problem or a pure math problem?+
Usually both. If the input is a state or grid, it's BFS or Dijkstra. If it's a number (e.g., reduce X to Y), check if math operations (divide, subtract) are the moves. The problem text will clarify. Read constraints first: if N is under 10,000, brute BFS works; if N is larger, you need DP or math insight.
What's the difference between greedy and DP here?+
Greedy picks the best local move each time (fast, risky). DP explores all states and memoizes. For minimum moves, DP is safer unless the problem explicitly states greedy works (e.g., coin change with canonical coin sets). When in doubt, code DP or BFS; they're harder to fail on.
Should I start with simulation or jump to BFS?+
Simulation is a trap if the minimum is large. Always sketch the state space first. If states repeat or branch, use BFS or DP. If you're just applying the same rule repeatedly and it converges, simulation is fine. The OA will reward you for knowing the difference in 30 seconds, not guessing.
What if I can't figure out the pattern in the first 5 minutes?+
Write a brute-force BFS that explores all reachable states. It's safe: correct on small inputs, and you'll pass some test cases. Then optimize if time permits. Most OAs don't penalize partial correctness, and a working slow solution beats a blank.
How do I spot the trick without the full problem?+
Look for keywords in the problem statement: 'reduction' (DP or math), 'distance' (BFS), 'optimal' (greedy check first), 'sequence' (often simulation or DP). The trick is usually that naive iteration times out. The solution involves memoization, pruning, or mathematical shortcut.