Originally posted by: JorisK

Hi,

I'm having some trouble with my c++ CP model. In my objective I would like to do the following:

min sum_i distanceMatrix[ x_i ][ x_{i+1} ][ i ]

Here, x_i is a variable indicating the city I visit at time i and distanceMatrix is a 3 dimensional int array.

Here is part of my implementation:

==================

=================

 

The problem is that   obj += distanceMatrix[visitAt[i]][visitAt[i+1]][i]; is not allowed. The compiler says: "no match for operator[]". Any suggestions on how to fix this?

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Originally posted by: PhilippeLaborie

Hello,

There is indeed no such a multi-dimensional expression in the C++ API of CP Optimizer. You will need to flatten your 3D matrix "distanceMatrix" into a 1D array and index it with an expression computed from the 3 arguments that access the 3D matrix.

Philippe

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Originally posted by: JorisK

Thank you. I was able to solve the problem with your suggestion. For future reference, here is (part)  of my implementation:

 

const int N=tdTSP->nrVertices+1;

const int N2=N*N;

const int N3=N2*N;

 

//Create a flat distance matrix.

IloIntArray  flatDistanceArray(env, N3); //travel distance from (i,j,t)=i*n+j*n+t

for(int i=0; i<N3; i++)

flatDistanceArray[i]=0;

 

for(int j=1; j<tdTSP->nrVertices; j++){

flatDistanceArray[j]=tdTSP->getDistance(0,j,0);

}

 

for(int i=1; i<tdTSP->nrVertices; i++){

for(int j=1; j<tdTSP->nrVertices; j++){

if(i==j)

continue;

for(int t=1; t<tdTSP->nrVertices-1; t++){

flatDistanceArray[t*N2+i*N+j]=tdTSP->getDistance(i,j,t);

}

}

}

 

for(int i=1; i<tdTSP->nrVertices; i++){

int j=tdTSP->nrVertices;

int t=tdTSP->nrVertices-1;

flatDistanceArray[t*N2+i*N+j]=tdTSP->getDistance(i,j,t);

}

 

//Define variables

 

visitAt = IloIntVarArray(env, tdTSP->nrVertices+1, 1, tdTSP->nrVertices-1);

indexAt = IloIntVarArray(env, tdTSP->nrVertices, 0, N3);

 

//Define objective

IloIntVar obj_var= IloIntVar(env, 0, INF, "obj");

model.add( IloMinimize(env, obj_var) );

IloIntExpr obj(env);

for(int i=0; i<tdTSP->nrVertices; i++){

obj += flatDistanceArray[indexAt[i]];

}

model.add( obj_var == obj);

 

 

//Define constraints.

 

//1. Link visitAt and indexAt variables

for(int i=0; i<tdTSP->nrVertices; i++){

model.add(indexAt[i]==N2*i+visitAt[i]*N+visitAt[i+1]);

 

}

Some side notes:

1. The flat data structure is quite cumbersome to maintain. It would be nice if a future release of CP Optimizer would support n-dimensional element constraints; flattening a matrix can be automated behind the scenes.

2. This model has an extremely large memory consumption. My guess would be that this is due to the internal representation of the element constraints. 

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