I just started reading graph theory and was reading about graph coloring. This problem popped in my mind:
We have to color our undirected graph(not completely) with only 1 color so that number of colored nodes are maximized. We need to find this maximum number. I was able to formulate an approach for non cyclic graphs :
My approach : First we divide graph into isolated components and do this for each component. We make a dfs tree and make 2 dp arrays while traversing it so that root comes last :
dp[0][u]=sum(dp[1][visited children])
dp[1][u]=sum(dp[0][visited children])
ans=max(dp[1][root],dp[0][root])
dp[0][i] , dp[1][i] are initialized to 0,1 respectively.
Here 0 signifies uncolored and 1 signifies colored.
But this does not work for cyclic graphs as I have assumed that no visited children are connected.
Can someone guide me in the right direction on how to solve this problem for cyclic graphs(not by brute force)? Is it possible to modify my approach or do we need to come up with a different approach? Would a greedy approach like coloring a nodes with least edges work?
This problem is NP-Hard as well, and is known as maximum independent set problem.
A set S<=V is said to be Independent Set in a graph if for each two vertices u,v in S, there is no edge (u,v).
The maximum size of S (which is the number you are seeking) is called the independence number of the graph, and unfortunately finding it is NP-Hard.
So, unless P=NP, your algorithm fails for general purposes graphs.
Proving it is fairly simple, given a graph G=(V,E), create the complementary graph G'=(V,E') where (u,v) is in E' if and only if (u,v) is NOT in E.
Now, given a graph G, there is a clique of size k if and only if there is an independent set of size k in G', using the same vertices (since if (u,v) are two vertices the independent set, there is no edge (u,v) in E', and by definition there is an edge in E. Repeat for all vertices in the independent set, and you got a clique in G).
Since clique problem is NP-Hard, this makes this one such as well.
Related
For an undirected graph G if possible I want to convert G to a directed graph such that in the directed graph each vertex must have an indegree of at least 2.
I worked out that for it to be possible the amount of edges will need be at least 2|V| therefore will only be possible from at least 5 vertexs.
I also worked out that each vertex will be in a cycle with at least two other vertex,
but I tried using a modification of N/F but can't seem to think of an algorithm that can actually do the conversion (if possible).
Any help / guidance will be appreciated
Given an undirected, unweighted graph with n vertices and m edges, you can decide whether an orientation with minimum indegree at least 2 exists (and find it, if so) in O(m^3) time. If there are no vertices of degree 3, you can sometimes do this fairly simply in O(m) time.
If v is a vertex of degree 1, the task is impossible. If v has degree 2, both its edges must be out-edges, so we can delete 'v' from our graph for now. We do this operation repeatedly based on the new degrees; it's possible that we end up with new degree-3 vertices, but we can now assume all vertices have degree >= 3.
If all vertices are degree 4 or greater, the orientation must exist, and you can use a standard Eulerian circuit algorithm (available in many standard graph libraries) as described in this post. You may need to modify the graph first: Eulerian circuits only exist if all vertex degrees are even. There are always an even number of odd degree vertices: match them up in pairs in any way, adding an edge for each pair. Orienting the edges on the original graph based on the circuit gives a solution for your problem.
For graphs with degree-3 vertices, you can combine a standard algorithm with a nonstandard one. The standard algorithm is maximum-cardinality matching for general graphs, usually the 'Blossom algorithm', which can be found in standard graph libraries. The non-standard (i.e., probably not found in your graph library) algorithm is found in the paper On finding orientations with the fewest number of vertices with small out-degree, which reduces the problem of 'Find an orientation with the fewest outdegree-less-than-2 vertices' to the problem of finding a maximum matching in a graph with O(m) vertices. The algorithm is quite short, and simpler since our graph has minimum degree 3. This solves your problem for fewest-indegrees-less-than-2 after you flip all edge directions.
I've received a task to find an algorithm which divides a graph G(V,E) into pairs of neighboring edges (colors the graph, such that every pair of neighboring edges has the same color).
I've tried to tackle this problem by drawing out some random graphs and came to a few conclusions:
If a vertex is connected to 2(4,6,8...) vertices of degree 1, these make a pair of edges.
If a vertex of degree 1 is directly connected to a cycle, it doesn't matter which edge of the cycle is paired with the lone edge.
However, I couldn't come up with any other conclusions, so I tried a different approach. I thought about using DFS, finding articulation points and dividing graph into subgraphs with an even number of edges, because those should be dividable by this rule as well, and so on until I end up with only subgraphs of |E(G')| = 2.
Another thing I've come up with is to create a graph G', where E(G) = V(G') and V(G) = E(G'). That way I could get a graph, where I could remove pairs of vertices (former edges) either via DFS or always starting with leaf vertices along with their adjacent vertices.
The last technique is most appealing to me, but it seems to be the slowest one. Any feedback or tips on which of these methods would be the best is much appreciated.
EDIT: In other words, imagine the graph as a layout of a town. Vertices being crossroads, edges being the roads. We want to decorate (sweep, color) each road exactly once, but we can only decorate two connected roads at the same time. I hope this helps for clarification.
For example, having graph G with E={ab,bd,cd,ac,ae,be,bf,fd}, one of possible pair combinations is P={{ab,bf},{ac,cd},{ae,eb},{bd,df}}.
One approach is to construct a new graph G where:
A vertex in G corresponds to an edge in the original graph
An edge in G connects vertices a and b in G where a and b represent edges in the original graph that meet at a vertex in the original graph
Then, if I have understood the original problem correctly, the objective for G is to find the maximum matching, which can be done, for example, with the Blossom algorithm.
Background:
As you can see below, there is an undirected graph on the left of the figure. Vertices are represented by S1,S2 ... S6, and edges are represented by line segments between vertices. Every edge has a weight (the number near the edge), either positive or negative.
Definitions:
In the graph, a simple cycle is called a conflicting cycle if it has an odd number of negative edges, and a concordant cycle if an even (or zero) number of negative edges. On the left of the figure below, for example, the graph has two conflicting cycles(S1-S2-S3-S1 and S2-S3-S4-S2), and other cycles are concordant. A graph is called happy if it has no conflicting cycle.
Objective:
Make the graph happy by removing some edges, meanwhile ensuring the cost(the sum of absolute values of weights of removed edges) is lowest. In the figure below, for example, after removing the edge (red line segment), there is no conflicting cycles. So the graph becomes happy, and the cost is only 2.
This problem is NP-hard by reduction from maximum cut. Given an instance of maximum cut, multiply all of the edge weights by -1. The constraints of this problem dictate that edges be removed so as to eliminate all odd cycles, i.e., we need to find the maximum-weight bipartite subgraph.
This problem in fact is equivalent to a 2-label unique label cover problem. The goal is to color each vertex black or white so as to minimize the sum of costs for (i) positive edges that connect vertices of different colors (ii) negative edges that connect vertices of the same color. Deleting all of these edges is a valid solution to the original problem. Conversely, given a valid set of edges to delete, we can determine a coloring. I expect that there's an approximation algorithm based on semidefinite programming (and the relaxation could be used for branch and bound).
Unless you're familiar with combinatorial optimization, however, the algorithm that I would suggest is integer programming. Let x(e) be 1 if we delete edge e and let x(e) be 0 if we don't.
minimize sum_{edges e} cost(e) x(e)
subject to
for every simple cycle C with an odd number of negative edges,
sum_{edges e in C} x(e) >= 1
for each edge e, x(e) in {0, 1}
The solver will do most of the work. The problem is handling the exponential number of constraints that I've written. The crudest thing to do is to generate all simple cycles and give the solver the whole program. Another possibility is to solve to optimality with a subset of the constraints, test whether the solution is actually valid, and, if not, introduce one or more missing constraints. To do the test, attempt to two-color the undeleted subgraph such that vertices joined by a positive edge have identical colors and vertices joined by a negative edge have different colors. Color greedily; if we get stuck, then there's an odd cycle at fault.
With more sophistication, it's possible to solve the program as written via a technique called column generation.
I've written a solver for this problem (under the name "Signed Graph Balancing"). It is based on a fixed-parameter algorithm that is fast if only few edges need to be deleted. The method is described in the paper "Separator-based data reduction for signed graph balancing".
I have a grid based graph, where nodes and edges occupy cells. Edges can cross, but cannot travel on top of each other in the same direction.
Lets say I want to optimize the graph so that the distance covered by edges is minimized.
I am currently using A* search for each connection, but the algorithm is greedy and does not plan ahead. Consider the diagram below, where the order in which connections are made is changed (note also that there can be multiple shortest paths for any given edge, see green and
purple connections).
My intuition says this is NP-Complete and that an exhaustive search is necessary, which will be extremely expensive as the size of the graph grows. However, I have no way of showing this, and it is not quite the same as other graph embedding problems which usually concern minimization of crossing.
You didn't really describe your problem and your image is gone, but your problem sounds like the minimum T-join problem.
The minimum T-join problem is defined on a graph G. You're given a set T of even size, and you're trying to find a subgraph of the graph where the vertices of T have odd degree and the other vertices have even degree. You've got weights on the edges and you're trying to minimise the sum of the weights of edges in the subgraph.
Surprisingly, the minimum T-join problem can be solved in polynomial time thanks to a very close connection with the nonbipartite matching problem. Namely, if you find all-pairs shortest paths between vertices of T, the minimum T-join is attained by the minimum-weight perfect matching of vertices in T, where there's an edge between two vertices whose length is the length of the shortest path in G.
The minimum T-join will be a collection of paths. If two distinct paths, say a->b and c->d, use the same edge uv, then they can be replaced by a->u->c and b->v->d and reduce the cost of the T-join. So it won't use the same edge twice.
I have a disconnected bipartite undirected graph. I want to completely disconnect the graph. Only operation that I can perform is to remove a node. Removing a node will automatically delete its edges. Task is to minimize the number of nodes to be removed. Each node in the graph has atmost 4 edges.
By completely disconnecting a graph, I mean that no two nodes should be connected through a link. Basically an empty edge set.
I think, you cannot prove your algorithm is optimal because, in fact, it is not optimal.
To completely disconnect your graph minimizing the number of nodes to be removed, you have to remove all the nodes belonging to the minimal vertex cover of your graph. Searching the minimal vertex cover is usually NP-complete, but for bipartite graphs there is a polynomial-time solution.
Find maximum matching in the graph (probably with Hopcroft–Karp algorithm). Then use König's theorem to get the minimal vertex cover:
Consider a bipartite graph where the vertices are partitioned into left (L) and right (R) sets. Suppose there is a maximum matching which partitions the edges into those used in the matching (E_m) and those not (E_0). Let T consist of all unmatched vertices from L, as well as all vertices reachable from those by going left-to-right along edges from E_0 and right-to-left along edges from E_m. This essentially means that for each unmatched vertex in L, we add into T all vertices that occur in a path alternating between edges from E_0 and E_m.
Then (L \ T) OR (R AND T) is a minimum vertex cover.
Here's a counter-example to your suggested algorithm.
The best solution is to remove both nodes A and B, even though they are different colors.
Since all the edges are from one set to another, find these two sets using say BFS and coloring using 2 colours. Then remove the nodes in smaller set.
Since there are no edges among themselves the rest of the nodes are disconnected as well.
[As a pre-processing step you can leave out nodes with 0 edges first.]
I have thought of an algorithm for it but am not able to prove if its optimal.
My algorithm: On each disconnected subgraph, I run a BFS and color it accordingly. Then I identify the number of nodes colored with each color and take the minimum of the two and store. I repeat the procedure for each subgraph and add up to get the required minimum. Help me prove the algorithm if it's correct.
EDIT: The above algorithm is not optimal. The accepted answer has been verified to be correct.