Min Operations
Reported by candidates from Google's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
Google's Min Operations question hit the OA circuit in March 2024, and it's a pattern-matching problem disguised as an optimization puzzle. You're likely looking at a state-space search where the goal is to transform one value into another using a minimal set of allowed operations. The trick is recognizing which operations form a chain that reduces the problem size fastest. StealthCoder can surface the pattern instantly if you blank on the BFS or greedy approach during the live assessment.
Pattern and pitfall
Min Operations typically boils down to either breadth-first search (finding the shortest path through state transformations) or dynamic programming (building up the minimum cost to reach each state). The common pitfall is greedy thinking: just because one operation seems optimal locally doesn't mean it minimizes total steps. You need to either explore all reachable states level-by-level or compute the minimum operations bottom-up. The operations are usually simple (divide, multiply, add, subtract by 1) but the interaction between them creates the challenge. A BFS approach tracks visited states to avoid cycles. A DP approach fills a table or memo dictionary. Both work; BFS is often cleaner for this shape of problem.
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You can drill Min Operations cold, or you can hedge it. StealthCoder runs invisibly during screen share and surfaces a working solution in under 2 seconds. The proctor sees the IDE. They don't see what's behind it. If you're reading this with an OA window open, you're who this was built for.
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Min Operations FAQ
Is this a BFS or DP problem?+
Both can work. BFS explores states level-by-level and naturally finds the shortest path. DP computes the minimum operations to reach each value upward from the start. BFS is usually faster for small state spaces; DP scales better if you need to reuse subproblem solutions. Recognize the state space size first, then pick.
What's the trick Google is testing?+
They want to see if you can model the problem as a graph or decision tree, not just code a loop. Greedy fails here. You need to explore alternatives or compute all paths. The real test is recognizing that local optimization doesn't guarantee global optimality.
How do I avoid infinite loops or cycles?+
Track visited states in a set. For BFS, add states to the visited set when you enqueue them, not when you dequeue. For DP, compute in a defined order (e.g., from 1 to target) so you never revisit a state. Cycles happen when operations can reverse each other; visited prevents re-exploration.
What if the state space is huge?+
BFS will timeout if you're not careful. Prune states that exceed the target or that can't possibly lead to a better solution. DP with memoization is safer because it only computes what's needed. If even that's tight, look for a math pattern to skip redundant operations.
How much time do I have to solve this in the OA?+
Google OAs usually allow 30-45 minutes per problem. Min Operations is typically medium difficulty, so budget 20-30 minutes for correctness. If you're stuck on the approach after 10 minutes, outline BFS or DP pseudocode so you don't blank. StealthCoder gives you the safety net if panic sets in.