B. Dungeon Crawler

Unrestricted Tour

Forget the key and the trap for a moment. If the walk starts at $s$ and ends at some room $e$, then every edge outside the simple path $P(s,e)$ is used twice, while every edge on $P(s,e)$ is used once. Therefore the optimal unrestricted cost is \[ 2W - \mathrm{dist}(s,e), \] where $W$ is the sum of all edge weights.

So without the trap, we would simply choose the farthest possible endpoint from $s$.

What the Trap Changes

Assume the query is feasible, so $t$ is not on the path from $s$ to $k$.

Root the tree at $s$ and let $l$ be the lowest common ancestor of $k$ and $t$. The edges on the path from $l$ down to $k$ are special. Why?

To collect the key first, we must walk from $s$ to $k$. If we later want to finish in a place whose final simple path also uses some prefix of that same route, then those shared edges are no longer ``saved'' by ending there:

• first we traverse them while going from $s$ to $k$;

• then we backtrack over them to leave the key branch and handle the trap side;

• finally we may traverse them again on the way to the final endpoint.

• So an edge on this special path is used three times instead of once. Relative to the baseline cost $2W$, that means:

• a normal edge on the final path contributes a saving of $w$;

• a special edge on the final path contributes a penalty of $w$.

• Hence, for a chosen endpoint $e$, the effective saving is \[ \mathrm{dist}(s,e) - 2 \cdot \mathrm{len}\bigl(P(s,e) \cap P(l,k)\bigr). \]

  The answer is \[ 2W - \max_e \left( \mathrm{dist}(s,e) - 2 \cdot \mathrm{len}\bigl(P(s,e) \cap P(l,k)\bigr) \right). \]

Turning the Formula into an $O(n)$ Query

Fix the root $s$. Write the special path as \[ l \to a_1 \to a_2 \to \cdots \to a_r = k. \]

If the final endpoint leaves this branch immediately at $l$, then it touches none of the special edges and its saving is just its ordinary distance from $s$.

If the endpoint follows the branch down to $a_i$ and then leaves it there, then it uses exactly the first $i$ special edges, so the penalty is \[ 2 \cdot \mathrm{dist}(l, a_i). \] Inside the rooted tree at $s$, the relevant endpoints are:

• outside the subtree of $a_1$;

• inside the subtree of $a_i$ but outside the subtree of $a_{i+1}$ for $1 \le i < r$;

• anywhere inside the subtree of $k$.

• For a fixed root $s$, we precompute:

• $\text{dist}_s[v]$ and the parent of every node;

• $\text{subtreeBest}_s[v]$: maximum $\text{dist}_s[x]$ over the subtree of $v$;

• $\text{outsideBest}_s[v]$: maximum $\text{dist}_s[x]$ outside the subtree of $v$;

• $\text{branchBest}_s[v]$: if $p$ is the parent of $v$, the maximum $\text{dist}_s[x]$ inside the subtree of $p$ but outside the subtree of $v$.

• Then one query is handled by climbing from $k$ up toward $l$:

• endpoints outside the first special subtree use $\text{outsideBest}$ with penalty $0$;

• endpoints diverging at an internal special node use $\text{branchBest}$ minus the corresponding penalty;

• endpoints staying all the way inside the subtree of $k$ use $\text{subtreeBest}$ minus the full penalty.

• Since we only traverse the special path itself, each query costs $O(\text{length}(P(l,k))) \subseteq O(n)$.

Correctness Proof

We prove that the algorithm returns the optimal answer for every query.

Lemma 1.

A query is feasible if and only if $t$ does not lie on the path from $s$ to $k$.

Proof.

If $t$ lies on the path from $s$ to $k$, then every route from $s$ to $k$ enters $t$ before the key is collected, so the query is impossible.

If $t$ is not on that path, then we can walk from $s$ to $k$ first, collect the key, and from then on the trap restriction disappears. So the query is feasible. □

Lemma 2.

Fix a feasible query and a final endpoint $e$. Relative to the baseline cost $2W$, every edge on $P(s,e) \setminus P(l,k)$ contributes a saving of its weight, while every edge on $P(s,e) \cap P(l,k)$ contributes a penalty of its weight.

Proof.

Any edge not on the final simple path is still used twice, contributing exactly its baseline cost.

Consider an edge on $P(s,e)$ that is not special. It behaves exactly as in the unrestricted problem: we cross it once and never come back, so it saves one traversal.

Now consider a special edge, meaning it lies on both $P(s,k)$ and $P(k,t)$. We must cross it once while moving from $s$ to $k$. To later reach the trap side, we must cross it again in the opposite direction. If the final endpoint also lies beyond that edge on the key side, we cross it a third time while heading toward the endpoint. So compared with the baseline of twice, that edge costs one extra traversal instead of saving one. □

Lemma 3.

For any endpoint $e$, the minimum feasible walk ending at $e$ has length \[ 2W - \mathrm{dist}(s,e) + 2 \cdot \mathrm{len}\bigl(P(s,e) \cap P(l,k)\bigr). \]

Proof.

This is exactly the contribution count from Lemma 2, summed over all edges. No other edge changes its multiplicity compared with the baseline tour of length $2W$. □

Lemma 4.

If the endpoint first leaves the special branch at node $a_i$, then the penalty term is exactly $2 \cdot \mathrm{dist}(l,a_i)$.

Proof.

The shared part of the final path and the special path is precisely the prefix from $l$ down to $a_i$. Those are exactly the special edges that the final path keeps using. □

Theorem.

For every feasible query, the algorithm outputs the minimum total traversal time.

Proof.

By Lemma 1, infeasible queries are detected correctly.

For a feasible query, Lemma 3 shows that minimizing the walk length is equivalent to maximizing the effective saving. By Lemma 4, that saving depends only on where the final path leaves the special branch. The algorithm enumerates all possible departure regions:

• outside the first special subtree;

• each side region along the special branch;

• the subtree of $k$ itself.

• For each region it uses the farthest possible endpoint in that region and subtracts exactly the correct penalty. Therefore it computes the maximum feasible saving, and subtracting it from $2W$ gives the optimum answer. □

Complexity Analysis

For each possible start room $s$, we root the tree at $s$ once and compute all auxiliary tables in $O(n)$ time. Over all roots, preprocessing costs $O(n^2)$ time and $O(n^2)$ memory.

Each query climbs only the special path from $k$ to the branching point $l$, so it is answered in $O(n)$ time in the worst case.

Implementation Notes

• The helper on_path(a, b, c) checks whether $b$ lies on the path from $a$ to $c$ using the all-pairs distance table.

• branchBest[child] stores the best endpoint that stays inside the parent subtree but leaves the special branch immediately at that parent.