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花花酱 LeetCode 1514. Path with Maximum Probability

You are given an undirected weighted graph of n nodes (0-indexed), represented by an edge list where edges[i] = [a, b] is an undirected edge connecting the nodes a and b with a probability of success of traversing that edge succProb[i].

Given two nodes start and end, find the path with the maximum probability of success to go from start to end and return its success probability.

If there is no path from start to endreturn 0. Your answer will be accepted if it differs from the correct answer by at most 1e-5.

Example 1:

Input: n = 3, edges = [[0,1],[1,2],[0,2]], succProb = [0.5,0.5,0.2], start = 0, end = 2
Output: 0.25000
Explanation: There are two paths from start to end, one having a probability of success = 0.2 and the other has 0.5 * 0.5 = 0.25.

Example 2:

Input: n = 3, edges = [[0,1],[1,2],[0,2]], succProb = [0.5,0.5,0.3], start = 0, end = 2
Output: 0.30000

Example 3:

Input: n = 3, edges = [[0,1]], succProb = [0.5], start = 0, end = 2
Output: 0.00000
Explanation: There is no path between 0 and 2.


  • 2 <= n <= 10^4
  • 0 <= start, end < n
  • start != end
  • 0 <= a, b < n
  • a != b
  • 0 <= succProb.length == edges.length <= 2*10^4
  • 0 <= succProb[i] <= 1
  • There is at most one edge between every two nodes.

Solution: Dijkstra’s Algorithm

max(P1*P2*…*Pn) => max(log(P1*P2…*Pn)) => max(log(P1) + log(P2) + … + log(Pn) => min(-(log(P1) + log(P2) … + log(Pn)).

Thus we can convert this problem to the classic single source shortest path problem that can be solved with Dijkstra’s algorithm.

Time complexity: O(ElogV)
Space complexity: O(E+V)




花花酱 LeetCode 1489. Find Critical and Pseudo-Critical Edges in Minimum Spanning Tree

Given a weighted undirected connected graph with n vertices numbered from 0 to n-1, and an array edges where edges[i] = [fromi, toi, weighti] represents a bidirectional and weighted edge between nodes fromi and toi. A minimum spanning tree (MST) is a subset of the edges of the graph that connects all vertices without cycles and with the minimum possible total edge weight.

Find all the critical and pseudo-critical edges in the minimum spanning tree (MST) of the given graph. An MST edge whose deletion from the graph would cause the MST weight to increase is called a critical edge. A pseudo-critical edge, on the other hand, is that which can appear in some MSTs but not all.

Note that you can return the indices of the edges in any order.

Example 1:

Input: n = 5, edges = [[0,1,1],[1,2,1],[2,3,2],[0,3,2],[0,4,3],[3,4,3],[1,4,6]]
Output: [[0,1],[2,3,4,5]]
Explanation: The figure above describes the graph.
The following figure shows all the possible MSTs:

Notice that the two edges 0 and 1 appear in all MSTs, therefore they are critical edges, so we return them in the first list of the output.
The edges 2, 3, 4, and 5 are only part of some MSTs, therefore they are considered pseudo-critical edges. We add them to the second list of the output.

Example 2:

Input: n = 4, edges = [[0,1,1],[1,2,1],[2,3,1],[0,3,1]]
Output: [[],[0,1,2,3]]
Explanation: We can observe that since all 4 edges have equal weight, choosing any 3 edges from the given 4 will yield an MST. Therefore all 4 edges are pseudo-critical.


  • 2 <= n <= 100
  • 1 <= edges.length <= min(200, n * (n - 1) / 2)
  • edges[i].length == 3
  • 0 <= fromi < toi < n
  • 1 <= weighti <= 1000
  • All pairs (fromi, toi) are distinct.

Solution: Brute Force?

For each edge
1. exclude it and build a MST, cost increased => critical
2. for a non critical edge, force include it and build a MST, cost remains the same => pseudo critical

Proof of 2, if a non critical / non pseudo critical edge was added into the MST, the total cost must be increased. So if the cost remains the same, must be the other case. Since we know the edge is non-critical, so it has to be pseudo critical.


花花酱 LeetCode 1466. Reorder Routes to Make All Paths Lead to the City Zero

There are n cities numbered from 0 to n-1 and n-1 roads such that there is only one way to travel between two different cities (this network form a tree). Last year, The ministry of transport decided to orient the roads in one direction because they are too narrow.

Roads are represented by connections where connections[i] = [a, b] represents a road from city a to b.

This year, there will be a big event in the capital (city 0), and many people want to travel to this city.

Your task consists of reorienting some roads such that each city can visit the city 0. Return the minimum number of edges changed.

It’s guaranteed that each city can reach the city 0 after reorder.

Example 1:

Input: n = 6, connections = [[0,1],[1,3],[2,3],[4,0],[4,5]]
Output: 3
Explanation: Change the direction of edges show in red such that each node can reach the node 0 (capital).

Example 2:

Input: n = 5, connections = [[1,0],[1,2],[3,2],[3,4]]
Output: 2
Explanation: Change the direction of edges show in red such that each node can reach the node 0 (capital).

Example 3:

Input: n = 3, connections = [[1,0],[2,0]]
Output: 0


  • 2 <= n <= 5 * 10^4
  • connections.length == n-1
  • connections[i].length == 2
  • 0 <= connections[i][0], connections[i][1] <= n-1
  • connections[i][0] != connections[i][1]

Solution: BFS

Augment the graph
g[u][v] = 1, g[v][u] = 0, u->v is an edge in the original graph.

BFS from 0, sum up all the edge costs to visit all the nodes.

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


花花酱 LeetCode 1462. Course Schedule IV

There are a total of n courses you have to take, labeled from 0 to n-1.

Some courses may have direct prerequisites, for example, to take course 0 you have first to take course 1, which is expressed as a pair: [1,0]

Given the total number of courses n, a list of direct prerequisite pairs and a list of queries pairs.

You should answer for each queries[i] whether the course queries[i][0] is a prerequisite of the course queries[i][1] or not.

Return a list of boolean, the answers to the given queries.

Please note that if course a is a prerequisite of course b and course b is a prerequisite of course c, then, course a is a prerequisite of course c.

Example 1:

Input: n = 2, prerequisites = [[1,0]], queries = [[0,1],[1,0]]
Output: [false,true]
Explanation: course 0 is not a prerequisite of course 1 but the opposite is true.

Example 2:

Input: n = 2, prerequisites = [], queries = [[1,0],[0,1]]
Output: [false,false]
Explanation: There are no prerequisites and each course is independent.

Example 3:

Input: n = 3, prerequisites = [[1,2],[1,0],[2,0]], queries = [[1,0],[1,2]]
Output: [true,true]

Example 4:

Input: n = 3, prerequisites = [[1,0],[2,0]], queries = [[0,1],[2,0]]
Output: [false,true]

Example 5:

Input: n = 5, prerequisites = [[0,1],[1,2],[2,3],[3,4]], queries = [[0,4],[4,0],[1,3],[3,0]]
Output: [true,false,true,false]


  • 2 <= n <= 100
  • 0 <= prerequisite.length <= (n * (n - 1) / 2)
  • 0 <= prerequisite[i][0], prerequisite[i][1] < n
  • prerequisite[i][0] != prerequisite[i][1]
  • The prerequisites graph has no cycles.
  • The prerequisites graph has no repeated edges.
  • 1 <= queries.length <= 10^4
  • queries[i][0] != queries[i][1]

Solution: Floyd-Warshall Algorithm (All pairs shortest paths)

Time complexity: O(n^3 + q)
Space complexity: O(n^2)


花花酱 LeetCode 721. Accounts Merge

Given a list accounts, each element accounts[i] is a list of strings, where the first element accounts[i][0] is a name, and the rest of the elements are emails representing emails of the account.

Now, we would like to merge these accounts. Two accounts definitely belong to the same person if there is some email that is common to both accounts. Note that even if two accounts have the same name, they may belong to different people as people could have the same name. A person can have any number of accounts initially, but all of their accounts definitely have the same name.

After merging the accounts, return the accounts in the following format: the first element of each account is the name, and the rest of the elements are emails in sorted order. The accounts themselves can be returned in any order.

Example 1:

accounts = [["John", "", ""], ["John", ""], ["John", "", ""], ["Mary", ""]]
Output: [["John", '', '', ''],  ["John", ""], ["Mary", ""]]
The first and third John's are the same person as they have the common email "".
The second John and Mary are different people as none of their email addresses are used by other accounts.
We could return these lists in any order, for example the answer [['Mary', ''], ['John', ''], 
['John', '', '', '']] would still be accepted.

Note:The length of accounts will be in the range [1, 1000].The length of accounts[i] will be in the range [1, 10].The length of accounts[i][j] will be in the range [1, 30].

Solution: Union-Find