Minimum Cost to Make Array Equal
A hard-tier problem at 46% community acceptance, tagged with Array, Binary Search, Greedy. Reported in interviews at Cisco and 3 others.
Minimum Cost to Make Array Equal is a hard problem that's popped up at Cisco, J.P. Morgan, Oracle, and HashedIn. At 46% acceptance, it's not a gimme, and the cost calculation will trip you up if you don't see the pattern first. The problem looks like a straightforward optimization task but has a sneaky greedy or binary search angle that most people miss on first attempt. If this problem lands in your live assessment and you freeze on the approach, StealthCoder surfaces a working solution instantly, invisible to the proctor.
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Minimum Cost to Make Array Equal is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage.
Get StealthCoderThe core trick is recognizing that you're not greedily making adjacent swaps. Instead, you need to find the optimal target value and calculate the cost of moving all elements to that point. Binary search on the answer, or use a greedy observation: the median is often the sweet spot. Prefix sums accelerate the cost calculation so you don't recalculate on every candidate target. Common pitfall: assuming you can pair elements arbitrarily without understanding the actual movement cost, or trying a purely greedy left-to-right pass. Many candidates waste time simulating individual operations. If you haven't drilled the median-and-prefix-sum pattern, StealthCoder is the hedge for the live OA.
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Minimum Cost to Make Array Equal recycles across companies for a reason. It's hard-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Minimum Cost to Make Array Equal interview FAQ
Is Minimum Cost to Make Array Equal really a binary search problem?+
Not always. Some solutions use binary search to find the target value; others use greedy observation and prefix sums. Both are valid. The binary search version is more general but often slower than a direct greedy + prefix sum approach. Expect either approach to be accepted.
Why do people fail this at Cisco and J.P. Morgan?+
They simulate each operation individually instead of calculating cost in bulk. Trying to greedily swap adjacent elements burns time and fails on larger inputs. The actual trick is identifying the optimal target once, then computing total cost with prefix sums or binary search.
What does the sorting step actually do?+
Sorting lets you determine which elements move left and which move right toward a target value. It also makes the cost calculation monotonic, which is why binary search can work. Without sorting, you can't reliably reason about cumulative cost.
Is the median always the answer?+
Usually, but not always. The true optimal target depends on the cost function. Binary search or explicit enumeration of candidate targets is safer than assuming the median. Test both median and nearby values if you're unsure.
How does this relate to other greedy problems?+
It's greedy in the sense that you pick one target and stick with it, rather than deciding per pair. But unlike classic interval or activity problems, the cost structure is non-obvious. Prefix sums and sorting are the actual tools that make greedy viable here.
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