Given a directed graph G=(V,E) and a weight function w : E - > R+ (only positive weights for edges in the graph) , I need to find all the shortest paths from every vertex v in V to a given vertex k.
I've thought about reversing the edges in the graph and then running Dijkstra's algorithm from the vertex k. I wonder whether a shortest path p from k to v1 is actually the shortest path from v1 to k in the original graph ( before reversing edges ).
I'd be grateful if anyone could explain if and why it does / does not happen.
Thanks in advance.
(This won't be the most formal proof in the world, but hopefully its good enough to convince yourself).
Lets say for a vertex v, in graph G, the shortest path from v to k is of length m.
The two things you want to know are:
1. In the reversed graph, G*, there is a path of length m from k to v.
2. In the reversed graph, G*, there are no paths from k to v that are shorter than m.
Before I start, can we take one thing on faith:
Lemma 1: If you have a directed path from vertex v to vertex w, and you reverse every edge on the path, then you have a path from vertex w to vertex v. This is provable, but I think its fairly common sense. I'll prove it if you want me to.
For point 1: Consider the path in G from v to k consisting of m edges. If you reverse each of these edges, you will have a path from k to v of length m (by Lemma 1).
For point 2: Suppose there exists a path in the reversed graph G*, from k to v of length n < m. If you reverse this path, then there is a path of length n from v to k (Lemma 1). This means that there is a path from v to k in the original graph that is shorter than m, contradicting the statement that the path of length m is the shortest.
Related
Given a weighed, connected and directed graph G=(V,E) with n vertexes and m edges, and given a pre-calculated shortest path distance's matrix S where S is n*n S(i,j) denotes the weight of shortest path from vertex i to vertex j.
we know just weight of one edge (u, v) is changed (increased or decreased).
for two specific vertex s and t we want to update the shortest path length between these two vertex.
This can be done in O(1).
How is this possible? what is the trick of this answer?
You certainly can for decreases. I assume S will always refer to the old distances. Let l be the new distance between (u, v). Check if
S(s, u) + l + S(v, t) < S(s, t)
if yes then the left hand side is the new optimal distance between s and t.
Increases are impossible. Consider the following graph (edges in red have zero weight):
Suppose m is the minimum weight edge here, except for (u, v) which used to be lower. Now we update (u, v) to some weight l > m. This means we must find m to find the new optimum length.
Suppose we could do this in O(1) time. Then it means we could find the minimum of any array in O(1) time by feeding it into this algorithm after adding (u, v) with weight -BIGNUMBER and then 'updating' it to BIGNUMBER (we can lazily construct the distance matrix because all distances are either 0, inf or just the edge weights). That is clearly not possible, thus we can't solve this problem in O(1) either.
Given a weighted graph G=(V,E) which doesnt include negative cycles, a natural number k, and two verticles: s,t.
How can I find the cheapest route from s to t which its length can be divied by k?
Prepare a new graph G' with vertices V × {0, 1, …, n−1} and for each arc v → w of length ℓ in G, arcs (v, x) → (w, (x + ℓ) mod k). Then use Dijkstra's algorithm to find a shortest path from (s, 0) to (t, 0).
Use BFS with a priority queue, so as to always examine states (= paths from s) that are shortest. Unlike normal Dijkstra, your states are full paths, and you can revisit already-visited vertices as often as they are encountered.
I cannot prove that such an algorithm would be optimal, but at least it should be correct, always returning a valid shortest-path answer if it exists. Runtime for certain graphs and values of K would be very high, and the algorithm may not finish at all if there are no k-divisible paths from s to t but there are loops with a path-length divisible by k. You could find and filter those out first by using a preliminary DFS.
I have this question
Given a directed graph G with positive edge weights and a landmark vertex x, your goal is to find the length of the shortest path from one vertex v to another vertex w that passes through the landmark x.
It is needed to Describe a O(E log V ) algorithm for the problem.
I know that the complexity of Dijkstra Algorithm is O(ElogV).
Please can you help me in how to start solving this problem.
If you first find the shortest path from v to x, p_1 and from x to w, p_2 using Dijkstra's Algorithm and take the concatenation of these paths, p, then this will be the shortest path from v to w through x.
If there were a shorter path, p', then splitting this path at x would yield a path from v to x, p_1' and one from x to w, p_2' where p_1' is shorter than p_1, or p_2' is shorter than p_2 (otherwise length(p_1'+p_2') > length(p_1+p_2)) which is a contradiction.
EDIT: This is obviously O(E logV) since it is just using Dijkstra twice.
Love some guidance on this problem:
G is a directed acyclic graph. You want to move from vertex c to vertex z. Some edges reduce your profit and some increase your profit. How do you get from c to z while maximizing your profit. What is the time complexity?
Thanks!
The problem has an optimal substructure. To find the longest path from vertex c to vertex z, we first need to find the longest path from c to all the predecessors of z. Each problem of these is another smaller subproblem (longest path from c to a specific predecessor).
Lets denote the predecessors of z as u1,u2,...,uk and dist[z] to be the longest path from c to z then dist[z]=max(dist[ui]+w(ui,z))..
Here is an illustration with 3 predecessors omitting the edge set weights:
So to find the longest path to z we first need to find the longest path to its predecessors and take the maximum over (their values plus their edges weights to z).
This requires whenever we visit a vertex u, all of u's predecessors must have been analyzed and computed.
So the question is: for any vertex u, how to make sure that once we set dist[u], dist[u] will never be changed later on? Put it in another way: how to make sure that we have considered all paths from c to u before considering any edge originating at u?
Since the graph is acyclic, we can guarantee this condition by finding a topological sort over the graph. topological sort is like a chain of vertices where all edges point left to right. So if we are at vertex vi then we have considered all paths leading to vi and have the final value of dist[vi].
The time complexity: topological sort takes O(V+E). In the worst case where z is a leaf and all other vertices point to it, we will visit all the graph edges which gives O(V+E).
Let f(u) be the maximum profit you can get going from c to u in your DAG. Then you want to compute f(z). This can be easily computed in linear time using dynamic programming/topological sorting.
Initialize f(u) = -infinity for every u other than c, and f(c) = 0. Then, proceed computing the values of f in some topological order of your DAG. Thus, as the order is topological, for every incoming edge of the node being computed, the other endpoints are calculated, so just pick the maximum possible value for this node, i.e. f(u) = max(f(v) + cost(v, u)) for each incoming edge (v, u).
Its better to use Topological Sorting instead of Bellman Ford since its DAG.
Source:- http://www.utdallas.edu/~sizheng/CS4349.d/l-notes.d/L17.pdf
EDIT:-
G is a DAG with negative edges.
Some edges reduce your profit and some increase your profit
Edges - increase profit - positive value
Edges - decrease profit -
negative value
After TS, for each vertex U in TS order - relax each outgoing edge.
dist[] = {-INF, -INF, ….}
dist[c] = 0 // source
for every vertex u in topological order
if (u == z) break; // dest vertex
for every adjacent vertex v of u
if (dist[v] < (dist[u] + weight(u, v))) // < for longest path = max profit
dist[v] = dist[u] + weight(u, v)
ans = dist[z];
We are given a directed graph G (possibly with cycles) with positive edge weights, and the minimum distance D[v] to every vertex v from a source s is also given (D is an array this way).
The problem is to find the array N[v] = number of paths of length D[v] from s to v,
in linear time.
Now this is a homework problem that I've been struggling with for quite long. I was working along the following thought : I'm trying to remove the cycles by suitably choosing an acyclic subgraph of G, and then try to find shortest paths from s to v in the subgraph.
But I cannot figure out explicitly what to do, so I'd appreciate any help, as in a qualitative idea on what to do.
You can use dynamic programming approach in here, and fill up the number of paths as you go, if D[u] + w(u,v) = D[v], something like:
N = [0,...,0]
N[s] = 1 //empty path
For each vertex v, in *ascending* order of `D[v]`:
for each edge (u,v) such that D[u] < D[v]:
if D[u] + w(u,v) = D[v]: //just found new shortest paths, using (u,v)!
N[v] += N[u]
Complexity is O(VlogV + E), assuming the graph is not sparsed, O(E) is dominanting.
Explanation:
If there is a shortest path v0->v1->...->v_(k-1)->v_k from v0 to v_k, then v0->...->v_(k-1) is a shortest path from v0 to v_k-1, thus - when iterating v_k - N[v_(k-1)] was already computed fully (remember, all edges have positive weights, and D[V_k-1] < D[v_k], and we are iterating by increasing value of D[v]).
Therefor, the path v0->...->v_(k-1) is counted in the number N[V_(k-1)] at this point.
Since v0->...->v_(k-1)-v_k is a shortest path - it means D[v_(k-1)] + w(v_k-1,v_k) = D[v_k] - thus the condition will hold, and we will add the count of this path to N[v_k].
Note that the proof for this algorithm will basically be induction that will follow the guidelines from this explanation more formally.