Maximum Matrix Sum
A medium-tier problem at 66% community acceptance, tagged with Array, Greedy, Matrix. Reported in interviews at Honeywell and 0 others.
You're looking at a matrix problem that sounds straightforward until you hit the twist: maximize the sum of absolute values while flipping signs. Honeywell has asked this one. The trap most candidates fall into is treating it like a standard greedy problem where you just pick the biggest numbers. That fails because flipping a single negative to positive might flip an adjacent cell to negative, and now you're stuck with one unavoidable negative in your final answer. You need to see the pattern: it's not about which cells to flip, it's about which single cell ends up negative in the optimal solution. StealthCoder runs invisibly during your assessment and surfaces the greedy insight in seconds if you blank on it.
Companies that ask "Maximum Matrix Sum"
Maximum Matrix Sum 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 because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround.
Get StealthCoderThe key insight is that you can flip signs on adjacent cells to propagate changes across the matrix. This means you can make almost any cell positive except one. The greedy strategy: first, flip every negative number to positive by propagating sign flips through neighbors. Then you're left with one forced negative cell somewhere. To minimize damage, that cell should be the smallest absolute value in your matrix. So the algorithm is simple: sum all absolute values, then subtract twice the minimum absolute value (flipping it from positive back to negative). The edge case is when the smallest absolute value is already negative in the original matrix and you can't avoid it. Most candidates overthink the flipping mechanics and miss that you only need to track absolute values. If you hit this live and the flipping pattern isn't clicking, StealthCoder shows the solution immediately, no proctor visibility.
Pattern tags
You know the problem.
Make sure you actually pass it.
Maximum Matrix Sum recycles across companies for a reason. It's medium-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 because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Matrix Sum interview FAQ
Is this actually a medium, or is it harder?+
The acceptance rate sits at 66 percent, which is slightly above typical medium difficulty. It's genuinely medium once you see the pattern, but the twist catches most people off guard initially. The greedy insight is the hard part, the implementation is trivial once you have it.
Do I actually need to simulate flipping adjacent cells?+
No. That's the trap. You don't simulate anything. You recognize that the flipping mechanic is just a way to say 'you can control signs almost completely.' The actual solution is sum all absolute values, subtract twice the minimum absolute value. No simulation needed.
What if all numbers are negative?+
You still sum the absolute values (which makes them positive) and subtract twice the smallest one. That smallest becomes your unavoidable negative. This is the same algorithm, no special case. The constraint is always there: exactly one cell ends up negative in the optimal answer.
Why does Greedy work here?+
Greedy works because the flipping mechanic lets you propagate sign changes to any cell. This means maximizing the matrix sum reduces to: maximize absolute value contribution, then minimize the loss from one forced negative. Always pick the smallest absolute value to be negative. That's optimal.
Is this asked frequently at other companies?+
Honeywell is the only company in the reported data for this problem. It's not a FAANG staple, but the pattern (greedy plus a twist constraint) is common. If you see it in an assessment, the same insight applies: look for what you can't control and optimize around that.
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