Posts published in “Graph”

花花酱 LeetCode 2662. Minimum Cost of a Path With Special Roads

By zxi on April 30, 2023

You are given an array start where start = [startX, startY] represents your initial position (startX, startY) in a 2D space. You are also given the array target where target = [targetX, targetY] represents your target position (targetX, targetY).

The cost of going from a position (x1, y1) to any other position in the space (x2, y2) is |x2 - x1| + |y2 - y1|.

There are also some special roads. You are given a 2D array specialRoads where specialRoads[i] = [x1_i, y1_i, x2_i, y2_i, cost_i] indicates that the i^th special road can take you from (x1_i, y1_i) to (x2_i, y2_i) with a cost equal to cost_i. You can use each special road any number of times.

Return the minimum cost required to go from (startX, startY) to (targetX, targetY).

Example 1:

Input: start = [1,1], target = [4,5], specialRoads = [[1,2,3,3,2],[3,4,4,5,1]]
Output: 5
Explanation: The optimal path from (1,1) to (4,5) is the following:
- (1,1) -> (1,2). This move has a cost of |1 - 1| + |2 - 1| = 1.
- (1,2) -> (3,3). This move uses the first special edge, the cost is 2.
- (3,3) -> (3,4). This move has a cost of |3 - 3| + |4 - 3| = 1.
- (3,4) -> (4,5). This move uses the second special edge, the cost is 1.
So the total cost is 1 + 2 + 1 + 1 = 5.
It can be shown that we cannot achieve a smaller total cost than 5.

Example 2:

Input: start = [3,2], target = [5,7], specialRoads = [[3,2,3,4,4],[3,3,5,5,5],[3,4,5,6,6]]
Output: 7
Explanation: It is optimal to not use any special edges and go directly from the starting to the ending position with a cost |5 - 3| + |7 - 2| = 7.

Constraints:

start.length == target.length == 2
1 <= startX <= targetX <= 10⁵
1 <= startY <= targetY <= 10⁵
1 <= specialRoads.length <= 200
specialRoads[i].length == 5
startX <= x1_i, x2_i <= targetX
startY <= y1_i, y2_i <= targetY
1 <= cost_i <= 10⁵

Solution: Dijkstra

Create a node for each point in special edges as well as start and target.
Add edges for special roads (note it’s directional)
Add edges for each pair of node with default cost, i.e. |x1-x2| + |y1-y2|
Run Dijkstra’s algorithm

Time complexity: O(n²logn)
Space complexity: O(n²)

C++

// Author: Huahua

class Solution {

public:

int minimumCost(vector<int>& start, vector<int>& target, vector<vector<int>>& specialRoads) {

unordered_map<long, int> ids;

vector<pair<int, int>> pos;

auto getId = [&](int x, int y) {

const auto key = ((unsigned long)x << 32) | y;

if (ids.count(key)) return ids[key];

const int id = ids.size();

ids[key] = id;

pos.emplace_back(x, y);

return id;

};

const int s = getId(start[0], start[1]);

const int t = getId(target[0], target[1]);

vector<vector<pair<int, int>>> g;

auto addEdge = [&](int x1, int y1, int x2, int y2, int c = INT_MAX) {

int n1 = getId(x1, y1);

int n2 = getId(x2, y2);

if (n1 == n2) return;

while (g.size() <= max(n1, n2)) g.push_back({});

int cost = min(c, abs(x1 - x2) + abs(y1 - y2));

g[n1].emplace_back(cost, n2);

// directional for special edge

if (c == INT_MAX) g[n2].emplace_back(cost, n1);

};

for (const auto& r : specialRoads)

addEdge(r[0], r[1], r[2], r[3], r[4]);

for (int i = 0; i < pos.size(); ++i)

for (int j = i + 1; j < pos.size(); ++j)

addEdge(pos[i].first, pos[i].second,

pos[j].first, pos[j].second);

vector<int> dist(ids.size(), INT_MAX);

dist[s] = 0;

priority_queue<pair<int, int>, vector<pair<int, int>>, greater<>> q;

q.emplace(0, s);

while (!q.empty()) {

auto [d, u] = q.top(); q.pop();

if (d > dist[u]) continue;

if (u == t) return d;

for (auto [c, v] : g[u])

if (d + c < dist[v])

q.emplace(dist[v] = d + c, v);

}

return -1;

}

};

花花酱 LeetCode 2658. Maximum Number of Fish in a Grid

By zxi on April 30, 2023

You are given a 0-indexed 2D matrix grid of size m x n, where (r, c) represents:

A land cell if grid[r][c] = 0, or
A water cell containing grid[r][c] fish, if grid[r][c] > 0.

A fisher can start at any water cell (r, c) and can do the following operations any number of times:

Catch all the fish at cell (r, c), or
Move to any adjacent water cell.

Return the maximum number of fish the fisher can catch if he chooses his starting cell optimally, or 0 if no water cell exists.

An adjacent cell of the cell (r, c), is one of the cells (r, c + 1), (r, c - 1), (r + 1, c) or (r - 1, c) if it exists.

Example 1:

Input: grid = [[0,2,1,0],[4,0,0,3],[1,0,0,4],[0,3,2,0]]
Output: 7
Explanation: The fisher can start at cell (1,3) and collect 3 fish, then move to cell (2,3) and collect 4 fish.

Example 2:

Input: grid = [[1,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,1]]
Output: 1
Explanation: The fisher can start at cells (0,0) or (3,3) and collect a single fish.

Constraints:

m == grid.length
n == grid[i].length
1 <= m, n <= 10
0 <= grid[i][j] <= 10

Solution: Connected Component

Find the connected component that has the max sum.

Time complexity: O(mn)
Space complexity: O(mn)

C++

// Author: Huahua

class Solution {

public:

int findMaxFish(vector<vector<int>>& grid) {

const int m = grid.size();

const int n = grid[0].size();

function<int(int, int)> dfs = [&](int r, int c) {

if (r < 0 || r >= m || c < 0 || c >= n || !grid[r][c]) return 0;

return exchange(grid[r][c], 0)

+ dfs(r - 1, c) + dfs(r, c - 1) + dfs(r + 1, c) + dfs(r, c + 1);

};

int ans = 0;

for (int r = 0; r < m; ++r)

for (int c = 0; c < n; ++c)

ans = max(ans, dfs(r, c));

return ans;

}

};

花花酱 LeetCode 2642. Design Graph With Shortest Path Calculator

By zxi on April 29, 2023

There is a directed weighted graph that consists of n nodes numbered from 0 to n - 1. The edges of the graph are initially represented by the given array edges where edges[i] = [from_i, to_i, edgeCost_i] meaning that there is an edge from from_i to to_i with the cost edgeCost_i.

Implement the Graph class:

Graph(int n, int[][] edges) initializes the object with n nodes and the given edges.
addEdge(int[] edge) adds an edge to the list of edges where edge = [from, to, edgeCost]. It is guaranteed that there is no edge between the two nodes before adding this one.
int shortestPath(int node1, int node2) returns the minimum cost of a path from node1 to node2. If no path exists, return -1. The cost of a path is the sum of the costs of the edges in the path.

Example 1:

Input
["Graph", "shortestPath", "shortestPath", "addEdge", "shortestPath"]
[[4, [[0, 2, 5], [0, 1, 2], [1, 2, 1], [3, 0, 3]]], [3, 2], [0, 3], [[1, 3, 4]], [0, 3]]
Output
[null, 6, -1, null, 6]

Explanation

Graph g = new Graph(4, [[0, 2, 5], [0, 1, 2], [1, 2, 1], [3, 0, 3]]); g.shortestPath(3, 2); // return 6. The shortest path from 3 to 2 in the first diagram above is 3 -> 0 -> 1 -> 2 with a total cost of 3 + 2 + 1 = 6.
g.shortestPath(0, 3); // return -1. There is no path from 0 to 3. 
g.addEdge([1, 3, 4]); // We add an edge from node 1 to node 3, and we get the second diagram above. 
g.shortestPath(0, 3); // return 6. The shortest path from 0 to 3 now is 0 -> 1 -> 3 with a total cost of 2 + 4 = 6.

Constraints:

1 <= n <= 100
0 <= edges.length <= n * (n - 1)
edges[i].length == edge.length == 3
0 <= from_i, to_i, from, to, node1, node2 <= n - 1
1 <= edgeCost_i, edgeCost <= 10⁶
There are no repeated edges and no self-loops in the graph at any point.
At most 100 calls will be made for addEdge.
At most 100 calls will be made for shortestPath.

Solution 1: Floyd-Washall

Time complexity:
Init O(n³)
addEdge O(n²)
shortestPath O(1)

Space complexity: O(1)

C++

// Author: Huahua

class Graph {

public:

Graph(int n, vector<vector<int>>& edges):

n_(n),

d_(n, vector<long>(n, 1e18)) {

for (int i = 0; i < n; ++i)

d_[i][i] = 0;

for (const vector<int>& e : edges)

d_[e[0]][e[1]] = e[2];

for (int k = 0; k < n; ++k)

for (int i = 0; i < n; ++i)

for (int j = 0; j < n; ++j)

d_[i][j] = min(d_[i][j], d_[i][k] + d_[k][j]);

}

void addEdge(vector<int> edge) {

for (int i = 0; i < n_; ++i)

for (int j = 0; j < n_; ++j)

d_[i][j] = min(d_[i][j], d_[i][edge[0]] + edge[2] + d_[edge[1]][j]);

}

int shortestPath(int node1, int node2) {

return d_[node1][node2] >= 1e18 ? -1 : d_[node1][node2];

}

private:

int n_;

vector<vector<long>> d_;

};

Solution 2: Dijkstra

Time complexity:
Init: O(|E|) ~ O(n²)
AddEdge: O(1)
ShortestPath: O(|V|*log(|E|)) ~ O(n*logn)

Space complexity: O(E|) ~ O(n²)

C++

// Author: Huahua

class Graph {

public:

Graph(int n, vector<vector<int>>& edges): n_(n), g_(n) {

for (const auto& e : edges)

g_[e[0]].emplace_back(e[1], e[2]);

}

void addEdge(vector<int> edge) {

g_[edge[0]].emplace_back(edge[1], edge[2]);

}

int shortestPath(int node1, int node2) {

vector<int> dist(n_, INT_MAX);

dist[node1] = 0;

priority_queue<pair<int, int>, vector<pair<int, int>>, greater<>> q;

q.emplace(0, node1);

while (!q.empty()) {

const auto [d, u] = q.top(); q.pop();

if (u == node2) return d;

for (const auto& [v, c] : g_[u])

if (dist[u] + c < dist[v]) {

dist[v] = dist[u] + c;

q.emplace(dist[v], v);

}

return -1;

}

private:

int n_;

vector<vector<pair<int, int>>> g_;

};

花花酱 LeetCode 1061. Lexicographically Smallest Equivalent String

By zxi on January 14, 2023

You are given two strings of the same length s1 and s2 and a string baseStr.

We say s1[i] and s2[i] are equivalent characters.

For example, if s1 = "abc" and s2 = "cde", then we have 'a' == 'c', 'b' == 'd', and 'c' == 'e'.

Equivalent characters follow the usual rules of any equivalence relation:

Reflexivity: 'a' == 'a'.
Symmetry: 'a' == 'b' implies 'b' == 'a'.
Transitivity: 'a' == 'b' and 'b' == 'c' implies 'a' == 'c'.

For example, given the equivalency information from s1 = "abc" and s2 = "cde", "acd" and "aab" are equivalent strings of baseStr = "eed", and "aab" is the lexicographically smallest equivalent string of baseStr.

Return the lexicographically smallest equivalent string of baseStr by using the equivalency information from s1 and s2.

Example 1:

Input: s1 = "parker", s2 = "morris", baseStr = "parser"
Output: "makkek"
Explanation: Based on the equivalency information in s1 and s2, we can group their characters as [m,p], [a,o], [k,r,s], [e,i].
The characters in each group are equivalent and sorted in lexicographical order.
So the answer is "makkek".

Example 2:

Input: s1 = "hello", s2 = "world", baseStr = "hold"
Output: "hdld"
Explanation: Based on the equivalency information in s1 and s2, we can group their characters as [h,w], [d,e,o], [l,r].
So only the second letter 'o' in baseStr is changed to 'd', the answer is "hdld".

Example 3:

Input: s1 = "leetcode", s2 = "programs", baseStr = "sourcecode"

Output: "aauaaaaada"

Explanation: We group the equivalent characters in s1 and s2 as [a,o,e,r,s,c], [l,p], [g,t] and [d,m], thus all letters in baseStr except 'u' and 'd' are transformed to 'a', the answer is "aauaaaaada".

Constraints:

1 <= s1.length, s2.length, baseStr <= 1000
s1.length == s2.length
s1, s2, and baseStr consist of lowercase English letters.

Solution: Union Find

Time complexity: O(n + m)
Space complexity: O(1)

C++

// Author: Huahua

class Solution {

public:

string smallestEquivalentString(string s1, string s2, string baseStr) {

vector<int> p(26);

iota(begin(p), end(p), 0);

function<int(int)> find = [&](int x) {

return p[x] == x ? x : p[x] = find(p[x]);

};

for (int i = 0; i < s1.length(); ++i) {

int r1 = find(s1[i] - 'a');

int r2 = find(s2[i] - 'a');

if (r2 < r1) swap(r1, r2);

p[r2] = r1;

}

string ans(baseStr);

for (int i = 0; i < baseStr.length(); ++i) {

ans[i] = find(baseStr[i] - 'a') + 'a';

}

return ans;

}

};

花花酱 LeetCode 2477. Minimum Fuel Cost to Report to the Capital

By zxi on November 26, 2022

There is a tree (i.e., a connected, undirected graph with no cycles) structure country network consisting of n cities numbered from 0 to n - 1 and exactly n - 1 roads. The capital city is city 0. You are given a 2D integer array roads where roads[i] = [a_i, b_i] denotes that there exists a bidirectional road connecting cities a_i and b_i.

There is a meeting for the representatives of each city. The meeting is in the capital city.

There is a car in each city. You are given an integer seats that indicates the number of seats in each car.

A representative can use the car in their city to travel or change the car and ride with another representative. The cost of traveling between two cities is one liter of fuel.

Return the minimum number of liters of fuel to reach the capital city.

Example 1:

Input: roads = [[0,1],[0,2],[0,3]], seats = 5
Output: 3
Explanation: 
- Representative₁ goes directly to the capital with 1 liter of fuel.
- Representative₂ goes directly to the capital with 1 liter of fuel.
- Representative₃ goes directly to the capital with 1 liter of fuel.
It costs 3 liters of fuel at minimum. 
It can be proven that 3 is the minimum number of liters of fuel needed.

Example 2:

Input: roads = [[3,1],[3,2],[1,0],[0,4],[0,5],[4,6]], seats = 2
Output: 7
Explanation: 
- Representative₂ goes directly to city 3 with 1 liter of fuel.
- Representative₂ and representative₃ go together to city 1 with 1 liter of fuel.
- Representative₂ and representative₃ go together to the capital with 1 liter of fuel.
- Representative₁ goes directly to the capital with 1 liter of fuel.
- Representative₅ goes directly to the capital with 1 liter of fuel.
- Representative₆ goes directly to city 4 with 1 liter of fuel.
- Representative₄ and representative₆ go together to the capital with 1 liter of fuel.
It costs 7 liters of fuel at minimum. 
It can be proven that 7 is the minimum number of liters of fuel needed.

Example 3:

Input: roads = [], seats = 1
Output: 0
Explanation: No representatives need to travel to the capital city.

Constraints:

1 <= n <= 10⁵
roads.length == n - 1
roads[i].length == 2
0 <= a_i, b_i < n
a_i != b_i
roads represents a valid tree.
1 <= seats <= 10⁵

Solution: Greedy + DFS

To reach the minimum cost, we must share cars if possible, say X reps from children nodes to an intermediate node u on the way towards capital 0. Then they all changes cars at node u, and we need (X + 1) // seats cars/fuel from u to 0.

We use DFS to count # of reps at each node u while accumulating the total cost.

Time complexity: O(n)
Space complexity: O(n)

C++

// Author: Huahua

class Solution {

public:

long long minimumFuelCost(vector<vector<int>>& roads, int seats) {

long long ans = 0;

vector<vector<int>> g(roads.size() + 1);

for (const vector<int>& r : roads) {

g[r[0]].push_back(r[1]);

g[r[1]].push_back(r[0]);

}

// Returns total # of children of u.

function<int(int, int)> dfs = [&](int u, int p, int rep = 1) {

for (int v : g[u])

if (v != p) rep += dfs(v, u);

if (u) ans += (rep + seats - 1) / seats;

return rep;

};

dfs(0, -1);

return ans;

}

};