Solver Input

The problem data is organized and passed to the solver in an instance of the class TspInstance<V,E>. Note the use of generics. Here V is the type of the objects that will correspond to vertices and E is the type of the objects that will correspond to edges. The point of generics is that our code will not really depend on what the types of our nodes and edges are. Lets take a quick look at the highlights of the public interface to this class.

public UndirectedGraph<V, E> getGraph() {
	...
}
public Function<E, Integer> getEdgeWeights() {
	...
}

The UndirectedGraph<V,E> returned by getGraph() is from the JUNG library. You can view the complete documentation here (this page might be more helpful), but some important highlights are

Name	Return Type	Arguments	Description
addVertex	boolean	V vertex	Adds vertex to this graph. Fails if vertex is null or already in the graph.
addEdge	boolean	E e, V v1, V v2	Adds edge e to this graph such that it connects vertex v1 to v2.
findEdge	E	V v1, V v2	Returns an edge that connects vertex v1 and vertex v2. Returns null if either v2 is not connected to v1 or either v1 or v2 are not present in this graph.
getIncidentEdges	Collection<E>	V vertex	Returns the collection of edges in this graph which are connected to vertex.
getVertices	Collection<V>		Returns a view of all vertices in this graph.
getEdges	Collection<E>		Returns a view of all edges in this graph.

The Function<E,Integer> will give the cost of any edge for our optimization. The interface is quite simple:

Name	Return Type	Arguments	Description
apply	Integer	E edge	Returns the result of applying this function to edge.

Solver Interface

Open the file src/solver/TspIpSolver.java by double clicking it from the Project Explorer. This is the file we will be primarily editing. It should contain the following (after the imports):
Toggle TspIpSolver.java

public class TspIpSolver<V,E> {
	
	public static enum Option{
		lazy,userCut, randomizedUserCut, christofidesApprox, christofidesHeuristic,twoOpt,incumbent;
	}
	
	public TspIpSolver(TspInstance<V,E> tspInstance) throws IloException{
		this(tspInstance,EnumSet.of(Option.lazy, Option.userCut, 
		Option.christofidesApprox, Option.christofidesHeuristic));
	}
	
	public TspIpSolver(TspInstance<V,E> tspInstance, EnumSet<Option> options) throws IloException{}
	
	public void solve() throws IloException{		
	}
	
	public ImmutableSet<E> getEdgesInOpt(){
		return null;
	}
	
	public double getOptVal(){
		return 0;
	}	
}

For now, nothing in this class does much. Below, we summarize how all the parts should work. The enum Option is a collection of flags that can be passed in to the solver indicating what special techniques the solver should use. We now briefly summarize what each option indicates:

Option Name	Description
lazy	Add a LazyConstraintCallback to check potential integer solutions to ensure that the cutset constraints are satisfied. Without this option, there is no guarantee that cutset constraints are checked.
userCut	Add a UserCutCallback to check fractional solutions to ensure that the cutset constraints are satisfied.
randomizedUserCut	A variant of userCut where only a random subset of the cutset constraints are checked. If flagged, userCut should not be flagged
christofidesApprox	Use Christofides Algorithm to give an initial solution to CPLEX.
christofidesHeuristic	Use a variant of Christofides Algorithm to generate integer solutions from fractional solutions.
twoOpt	Use the Two-Opt local search heuristic to improve solutions generated by christofidesApprox, christofidesHeuristic, or incumbent, if any are selected.
incumbent	Collect integer solutions found by CPLEX to have Two-Opt applied. Requires that twoOpt is flagged.

Next, the constructors for class TspIpSolver:

Arguments	Description
TspInstance<V,E> tspInstance, EnumSet<Option> options	Creates an instance of the solver according to options
TspInstance<V,E> tspInstance	Creates an instance of the solver with the default options {lazy, userCut, christofidesApprox, christofidesHeuristic}

Finally, the methods of the class should behave as follows:

Name	Return Type	Description
solve	void	Runs CPLEX to solve the TSP with problem data given by TspInstance. Do not call more than once.
getEdgesInOpt	ImmutableSet<E>	Returns the subset of the edges of the TspInstance that are in the optimal solution. Do not call until solve() has finished. Behavior is undefined otherwise.
getOptVal	double	Returns the value of the optimal solution. Do not call until solve() has finished. Behavior is undefined otherwise.

Now that we understand the problem, we can begin implementing a solution!

Child pages

Solver Specification

Solver Input

Solver Interface