Minimize Array Sum with K Operations

I’m not able to reliably locate or verify a unique, canonical problem titled “Minimize Array Sum with K Operations” from an Adobe interview with exactly the tags Heap, Greedy, Priority Queue, and therefore I can’t truthfully give you a guaranteed “full official statement with all examples and constraints” from that specific source.[1][3]

However, there is a very commonly cited problem on major interview and practice sites that matches your title and tags in spirit:

Given an array of integers and an integer $$K$$, you can perform at most $$K$$ operations. In each operation, pick any element $$a_i$$, replace it with $$\left\lfloor \frac{a_i}{2} \right\rfloor$$ (or equivalently subtract $$\left\lfloor \frac{a_i}{2} \right\rfloor$$ from it), and update the array. Minimize the sum of the array after at most $$K$$ such operations. A max‑heap / priority queue with a greedy strategy is typically used.[3]

That general family of problems (sometimes worded as “Minimize Sum of an Array by at most K reductions” or similar) appears on well‑known competitive programming sites, and is solved by repeatedly picking the current maximum element using a heap, reducing it, and pushing it back until you use up $$K$$ operations or further reductions no longer help.[3]

Because I can’t see the internal Adobe repository or the exact private prompt you’re referencing, I cannot guarantee that:

• The exact variable names
• The exact text of “Input format” / “Output format”
• All examples and constraints

match what you saw in an Adobe interview. Any attempt to reconstruct those verbatim from memory or from behind a paywalled / private interview database would risk being inaccurate and could also step into copyright issues.[7]

If you want, I can:

• Write a clean, interview‑ready version of this problem (statement, input/output spec, clear constraints, and several well‑designed examples) that is faithful to the standard heap/greedy formulation but clearly marked as a reconstructed practice problem, not an official Adobe statement.[3]
• Then derive and explain the optimal heap/greedy solution, with time/space complexity and code in your preferred language.[3]