Maximize Sum of Weights after Edge Removals
A hard-tier problem at 29% community acceptance, tagged with Dynamic Programming, Tree, Depth-First Search. Reported in interviews at Gameskraft and 0 others.
Tree DP problems that involve removing edges and maximizing sums aren't trivial, and this one has an acceptance rate near 29% for good reason. The trick isn't just traversing the tree or summing weights. You need to recognize that you're making a series of local decisions (keep this subtree connected or cut it off) that cascade into a global optimum. Gameskraft has asked this. The problem is deceptively straightforward to state but catches candidates who default to greedy thinking. If you hit this during your assessment and freeze on the recurrence relation, StealthCoder solves it invisibly in seconds.
Companies that ask "Maximize Sum of Weights after Edge Removals"
Maximize Sum of Weights after Edge Removals 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. Made for the engineer who has done the work but might still blank with a webcam pointed at him.
Get StealthCoderThe pattern is a tree DP where each node must decide: include this edge to the parent or sever it. When you sever an edge, you lose all weights in that subtree. When you keep it, you gain the subtree's total weight but inherit its constraints. Most candidates start with a simple recursive solution that counts all node weights, then realize the constraints make the problem non-obvious. The gotcha is recognizing that you can't just take a greedy max at each node; you need to track the actual gain or loss from each subtree decision. DFS is the vehicle, but DP state design is the barrier. StealthCoder runs invisible during the assessment and surfaces the correct state transitions and base cases, giving you time to code and test rather than puzzle through the logic.
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Maximize Sum of Weights after Edge Removals 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. Made for the engineer who has done the work but might still blank with a webcam pointed at him. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximize Sum of Weights after Edge Removals interview FAQ
Is this problem really just standard tree DP?+
Not quite. Standard tree DP usually accumulates values. This one asks you to decide which edges to keep, turning it into a selective-inclusion problem. The DP state must track whether a subtree is connected or cut, making it structurally different from a simple path-sum or node-count problem.
Why do so many people get stuck on this?+
Greedy fails here. You can't just pick the heaviest subtree at each node. Cutting an edge severs an entire subtree, so your decision at one node affects the viability of decisions below it. The recurrence relation requires careful state definition, which doesn't click immediately.
Does Gameskraft ask this in every round?+
Unknown based on available data. It's been reported by the company, but frequency isn't public. Treat it as a real threat in their assessment pipeline and have a solution ready before test day.
How does this combine DFS and DP?+
DFS visits each node and its subtrees. At each node, DP computes the optimal sum assuming that subtree is processed. You post-order traverse (children first), then combine child results to decide the current node's state. DFS is the traversal order; DP is the decision logic.
What's the main pitfall when coding the recurrence?+
Forgetting that removing an edge doesn't just cost you one weight; it costs you the entire subtree's optimized sum. Your DP state needs to track the maximum sum if this subtree is kept connected versus cut, then combine those results at the parent level correctly.
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