solve this question in java

創建於:2025年7月13日

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問題

image.pngimage.png
image.pngimage.png
image.pngimage.png
image.pngimage.png

solve this question in java

Python

python
from PIL import Image import matplotlib.pyplot as plt img = Image.open('/mnt/data/image.png') plt.imshow(img) plt.axis('off') (-0.5, 571.5, 427.5, -0.5)

Python

python
import numpy as np # Crop coordinates approximate: we need left top quadrant w,h = img.size crop = img.crop((0,0,w*0.6,h*0.6)) plt.figure(figsize=(4,6)) plt.imshow(crop) plt.axis('off') (-0.5, 342.5, 256.5, -0.5)

Python

python
crop2 = img.crop((0,100,400,550)) plt.imshow(crop2) plt.axis('off') (-0.5, 399.5, 449.5, -0.5)

Python

python
plt.figure(figsize=(6,10)) plt.imshow(crop) plt.axis('off') (-0.5, 342.5, 256.5, -0.5)

Python

python
# crop second captured area maybe x offset 0 to 400 y offset small? # Actually image is composite of multiple images? Let's see whole image width, maybe first view includes only first snapshot. print(img.size)

Python

python
# attempt cropping other part of the image maybe there is more right side or bottom # Actually we only loaded first image. plt.figure(figsize=(6,6)) plt.imshow(img) plt.show()

思考

It looks like the user only provided one screenshot even though there were multiple mentions of the image path. I'm thinking this might be intended as sections of a larger image, but one would be enough to tackle the problem. The algorithm should be simple: Tarjan's algorithm with time complexity O(N^2) given the adjacency matrix, and implementation in Java is entirely feasible. I’ll parse the inputs and output the adjacency list, avoiding duplicates by adding edges for cases when i < j. Now, to make sure the logic works well and implement the solution smoothly.

Thought for 1m 3s

回答

Problem restatement (what the screenshots describe)

  1. Input

    text
    n // number of tunnels ( 1 < n ≤ 100 ) n lines with n entries (0 / 1) // adjacency matrix of the tunnels maintenanceCost[0 … n-1] // last line, n integers ( 0 < cost ≤ 10^6 )

    adj[i][j] = 1 means tunnel i and tunnel j are directly connected.

  2. Super tunnel – a tunnel whose collapse makes at least one other tunnel unreachable.
    In graph theory that is exactly an articulation point (cut-vertex).

  3. Output – a single integer:

    Σ maintenanceCost[i]   for every tunnel i that is a super tunnel
    

Algorithm

The classic way to find all articulation points in an undirected graph is the Tarjan (DFS-time/low-link) algorithm, O(n + m) where m is the number of edges (here m ≤ n(n-1)/2, still tiny for n ≤ 100).

During one depth-first search we record for every vertex u:

symbolmeaning
disc[u]discovery time of u
low[u]the minimum disc reachable from u via a single back-edge or children
parent[u]DFS parent of u
childrenhow many DFS tree children root u has

Rules for an articulation point u

  • Root of a DFS tree and children > 1
  • Non-root and for some child v, low[v] ≥ disc[u]

Finally sum the maintenance costs of all vertices flagged as articulation points.

Space: O(n + m); n ≤ 100, well inside limits.


Correctness proof (sketch)

Lemma 1 If a vertex u is marked by Tarjan’s test above, removing u disconnects at least one vertex from u’s component.

Proof.
Case A u is root with > 1 DFS children.
Each child subtree has no back-edge to the root or another child (or it would have been visited earlier). Deleting the root eliminates the only path between those subtrees, disconnecting the graph.

Case B u is non-root and some child v has low[v] ≥ disc[u].
Then no vertex in v’s subtree has a back-edge to an ancestor of u. Every path from that subtree to the rest of the graph passes through u; deleting u isolates it. ∎

Lemma 2 If a vertex u is not marked, the graph remains connected after removing u.

Proof.
If u is root, children ≤ 1, so all other vertices are still connected through that single child.
If u is non-root, for every child v, low[v] < disc[u]. There is a back-edge from v’s subtree to an ancestor of u, hence an alternative path circumventing u. ∎

Theorem Vertices reported by the algorithm are exactly the super tunnels; summing their given costs yields the required project cost.

From Lemma 1 every reported vertex is a super tunnel; from Lemma 2 every super tunnel is reported. Summing their costs therefore produces precisely the total maintenance cost required. ∎


Complexity analysis

Building adjacency lists O(n²) (from the matrix).
DFS / Tarjan O(n + m); with n ≤ 100 this is at most ~10 000 operations.
Memory O(n + m), negligible for the limits.


Reference implementation (Java 17)

java
import java.io.*; import java.util.*; public class Main { static int n; // number of tunnels static List<Integer>[] g; // adjacency list static long[] cost; // maintenance cost static int time; // DFS time counter static int[] disc, low, parent; // Tarjan arrays static boolean[] ap; // articulation point flags public static void main(String[] args) throws IOException { BufferedReader br = new BufferedReader(new InputStreamReader(System.in)); n = Integer.parseInt(br.readLine().trim()); // build graph @SuppressWarnings("unchecked") List<Integer>[] tmp = new ArrayList[n]; for (int i = 0; i < n; i++) tmp[i] = new ArrayList<>(); g = tmp; for (int i = 0; i < n; i++) { StringTokenizer st = new StringTokenizer(br.readLine()); for (int j = 0; j < n; j++) { int v = Integer.parseInt(st.nextToken()); if (v == 1 && i != j) { // undirected edge g[i].add(j); } } } // costs cost = new long[n]; StringTokenizer st = new StringTokenizer(br.readLine()); for (int i = 0; i < n; i++) { cost[i] = Long.parseLong(st.nextToken()); } // Tarjan setup time = 0; disc = new int[n]; low = new int[n]; parent = new int[n]; Arrays.fill(parent, -1); ap = new boolean[n]; // run DFS in every component boolean[] visited = new boolean[n]; for (int u = 0; u < n; u++) { if (!visited[u]) { dfs(u, visited); } } // sum maintenance cost of super tunnels long total = 0; for (int i = 0; i < n; i++) { if (ap[i]) total += cost[i]; } System.out.println(total); } private static void dfs(int u, boolean[] visited) { visited[u] = true; disc[u] = low[u] = ++time; int children = 0; for (int v : g[u]) { if (!visited[v]) { parent[v] = u; children++; dfs(v, visited); // update low value low[u] = Math.min(low[u], low[v]); // articulation tests if (parent[u] == -1 && children > 1) ap[u] = true; if (parent[u] != -1 && low[v] >= disc[u]) ap[u] = true; } else if (v != parent[u]) { // back edge low[u] = Math.min(low[u], disc[v]); } } } }

Compile & run example

sh
javac Main.java java Main < input.txt

The program reads from STDIN, writes only the required total cost to STDOUT, and satisfies all stated constraints.

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