//* A SHORT VERSION
int main(int argc, char ** argv)
{
// s_InputFile = baseDir + <name of the input file>
string s_InputFile; //path of the input file
s_InputFile = baseDir;
s_InputFile += DIR_SEPARATOR;
s_InputFile += "Graphs";
s_InputFile += DIR_SEPARATOR;
s_InputFile += "column-compress.mtx";
//Generate and color the bipartite graph
BipartiteGraphPartialColoringInterface *g = new BipartiteGraphPartialColoringInterface(SRC_FILE, s_InputFile.c_str(), "AUTO_DETECTED");
//Do Partial-Distance-Two-Coloring the bipartite graph with the specified ordering
g->PartialDistanceTwoColoring("SMALLEST_LAST", "ROW_PARTIAL_DISTANCE_TWO");
/*Done with coloring. Below are possible things that you may
want to do after coloring:
//*/
/* 1. Check Partial Distance Two Coloring result
cout<<"Check Partial Distance Two coloring result ... "<<endl;
if(g->CheckPartialDistanceTwoColoring() == _FALSE) cout<<" FAILED"<<endl;
else cout<<" SUCCEEDED"<<endl;
Pause();
//*/
//* 2. Print coloring results
g->PrintPartialColoringMetrics();
Pause();
//*/
//* 3. Get the list of colorID of colored vertices (in this case, the left side of the bipartite graph)
vector<int> vi_VertexPartialColors;
g->GetVertexPartialColors(vi_VertexPartialColors);
//Print Partial Colors
g->PrintPartialColors();
Pause();
//*/
/* 4. Get seed matrix
int i_SeedRowCount = 0;
int i_SeedColumnCount = 0;
double** Seed = g->GetSeedMatrix(&i_SeedRowCount, &i_SeedColumnCount);
//Display Seed
printf("Seed matrix %d x %d \n", i_SeedRowCount, i_SeedColumnCount);
displayMatrix(Seed, i_SeedRowCount, i_SeedColumnCount, 1);
Pause();
//*/
delete g;
return 0;
}
void generate_seed_jac
(int m, int n, unsigned int **JP, double*** Seed, int *p, int option
/* control options
option : way of compression
0 - column compression (default)
1 - row compression */
)
#if HAVE_LIBCOLPACK
{
int dummy;
BipartiteGraphPartialColoringInterface *g = new BipartiteGraphPartialColoringInterface(SRC_MEM_ADOLC, JP, m, n);
if (option == 1)
g->GenerateSeedJacobian_unmanaged(Seed, p, &dummy,
"SMALLEST_LAST","ROW_PARTIAL_DISTANCE_TWO");
else
g->GenerateSeedJacobian_unmanaged(Seed, &dummy, p,
"SMALLEST_LAST","COLUMN_PARTIAL_DISTANCE_TWO");
delete g;
}
int main()
{
double*** dp3_Seed = new double**;
int *ip1_SeedRowCount = new int;
int *ip1_SeedColumnCount = new int;
int i_RowCount, i_ColumnCount, i_MaxNonZerosInRows;
//populate the Jacobian. Uncomment one of the 2 matrices below
/* 1x1 matrix
i_RowCount = 1;
i_ColumnCount = 1;
i_MaxNonZerosInRows = 1;
unsigned int **uip2_JacobianSparsityPattern = new unsigned int *[i_RowCount];//[1][1]
for(int i=0;i<i_RowCount;i++) uip2_JacobianSparsityPattern[i] = new unsigned int[i_MaxNonZerosInRows + 1];
uip2_JacobianSparsityPattern[0][0] = 1; uip2_JacobianSparsityPattern[0][1] = 0;
//*/
//* 32x9 matrix
i_RowCount = 32;
i_ColumnCount = 9;
i_MaxNonZerosInRows = 3;
unsigned int **uip2_JacobianSparsityPattern = new unsigned int *[i_RowCount];//[32][9]
for(int i=0;i<i_RowCount;i++) uip2_JacobianSparsityPattern[i] = new unsigned int[i_MaxNonZerosInRows + 1];
uip2_JacobianSparsityPattern[0][0] = 0;
uip2_JacobianSparsityPattern[1][0] = 1; uip2_JacobianSparsityPattern[1][1] = 0;
uip2_JacobianSparsityPattern[2][0] = 1; uip2_JacobianSparsityPattern[2][1] = 1;
uip2_JacobianSparsityPattern[3][0] = 1; uip2_JacobianSparsityPattern[3][1] = 2;
uip2_JacobianSparsityPattern[4][0] = 1; uip2_JacobianSparsityPattern[4][1] = 0;
uip2_JacobianSparsityPattern[5][0] = 3; uip2_JacobianSparsityPattern[5][1] = 0; uip2_JacobianSparsityPattern[5][2] = 1; uip2_JacobianSparsityPattern[5][3] = 3;
uip2_JacobianSparsityPattern[6][0] = 3; uip2_JacobianSparsityPattern[6][1] = 1; uip2_JacobianSparsityPattern[6][2] = 2; uip2_JacobianSparsityPattern[6][3] = 4;
uip2_JacobianSparsityPattern[7][0] = 2; uip2_JacobianSparsityPattern[7][1] = 2; uip2_JacobianSparsityPattern[7][2] = 5;
uip2_JacobianSparsityPattern[8][0] = 1; uip2_JacobianSparsityPattern[8][1] = 3;
uip2_JacobianSparsityPattern[9][0] = 3; uip2_JacobianSparsityPattern[9][1] = 3; uip2_JacobianSparsityPattern[9][2] = 4; uip2_JacobianSparsityPattern[9][3] = 6;
uip2_JacobianSparsityPattern[10][0] = 3; uip2_JacobianSparsityPattern[10][1] = 4; uip2_JacobianSparsityPattern[10][2] = 5; uip2_JacobianSparsityPattern[10][3] = 7;
uip2_JacobianSparsityPattern[11][0] = 2; uip2_JacobianSparsityPattern[11][1] = 5; uip2_JacobianSparsityPattern[11][2] = 8;
uip2_JacobianSparsityPattern[12][0] = 1; uip2_JacobianSparsityPattern[12][1] = 6;
uip2_JacobianSparsityPattern[13][0] = 2; uip2_JacobianSparsityPattern[13][1] = 6; uip2_JacobianSparsityPattern[13][2] = 7;
uip2_JacobianSparsityPattern[14][0] = 2; uip2_JacobianSparsityPattern[14][1] = 7; uip2_JacobianSparsityPattern[14][2] = 8;
uip2_JacobianSparsityPattern[15][0] = 1; uip2_JacobianSparsityPattern[15][1] = 8;
uip2_JacobianSparsityPattern[16][0] = 1; uip2_JacobianSparsityPattern[16][1] = 0;
uip2_JacobianSparsityPattern[17][0] = 2; uip2_JacobianSparsityPattern[17][1] = 0; uip2_JacobianSparsityPattern[17][2] = 1;
uip2_JacobianSparsityPattern[18][0] = 2; uip2_JacobianSparsityPattern[18][1] = 1; uip2_JacobianSparsityPattern[18][2] = 2;
uip2_JacobianSparsityPattern[19][0] = 1; uip2_JacobianSparsityPattern[19][1] = 2;
uip2_JacobianSparsityPattern[20][0] = 2; uip2_JacobianSparsityPattern[20][1] = 0; uip2_JacobianSparsityPattern[20][2] = 3;
uip2_JacobianSparsityPattern[21][0] = 3; uip2_JacobianSparsityPattern[21][1] = 1; uip2_JacobianSparsityPattern[21][2] = 3; uip2_JacobianSparsityPattern[21][3] = 4;
uip2_JacobianSparsityPattern[22][0] = 3; uip2_JacobianSparsityPattern[22][1] = 2; uip2_JacobianSparsityPattern[22][2] = 4; uip2_JacobianSparsityPattern[22][3] = 5;
uip2_JacobianSparsityPattern[23][0] = 1; uip2_JacobianSparsityPattern[23][1] = 5;
uip2_JacobianSparsityPattern[24][0] = 2; uip2_JacobianSparsityPattern[24][1] = 3; uip2_JacobianSparsityPattern[24][2] = 6;
uip2_JacobianSparsityPattern[25][0] = 3; uip2_JacobianSparsityPattern[25][1] = 4; uip2_JacobianSparsityPattern[25][2] = 6; uip2_JacobianSparsityPattern[25][3] = 7;
uip2_JacobianSparsityPattern[26][0] = 3; uip2_JacobianSparsityPattern[26][1] = 5; uip2_JacobianSparsityPattern[26][2] = 7; uip2_JacobianSparsityPattern[26][3] = 8;
uip2_JacobianSparsityPattern[27][0] = 1; uip2_JacobianSparsityPattern[27][1] = 8;
uip2_JacobianSparsityPattern[28][0] = 1; uip2_JacobianSparsityPattern[28][1] = 6;
uip2_JacobianSparsityPattern[29][0] = 1; uip2_JacobianSparsityPattern[29][1] = 7;
uip2_JacobianSparsityPattern[30][0] = 1; uip2_JacobianSparsityPattern[30][1] = 8;
uip2_JacobianSparsityPattern[31][0] = 0;
//*/
//Step 1: Read the sparsity pattern of the given Jacobian matrix (compressed sparse rows format)
//and create the corresponding bipartite graph
BipartiteGraphPartialColoringInterface * g = new BipartiteGraphPartialColoringInterface(SRC_MEM_ADOLC, uip2_JacobianSparsityPattern, i_RowCount, i_ColumnCount);
//Step 2: Do Partial-Distance-Two-Coloring the bipartite graph with the specified ordering
g->PartialDistanceTwoColoring( "SMALLEST_LAST", "COLUMN_PARTIAL_DISTANCE_TWO");
//Step 3: From the coloring information, create and return the seed matrix
(*dp3_Seed) = g->GetSeedMatrix(ip1_SeedRowCount, ip1_SeedColumnCount);
/* Notes:
In stead of doing step 1-3, you can just call the bellow function:
g->GenerateSeedJacobian(uip2_JacobianSparsityPattern, i_RowCount,i_ColumnCount, dp3_Seed, ip1_SeedRowCount, ip1_SeedColumnCount, "COLUMN_PARTIAL_DISTANCE_TWO", "SMALLEST_LAST"); // compress columns. This function is inside BipartiteGraphPartialColoringInterface class
*/
cout<<"Finish GenerateSeed()"<<endl;
//this SECTION is just for displaying the result
g->PrintBipartiteGraph();
g->PrintColumnPartialColors();
g->PrintColumnPartialColoringMetrics();
double **RSeed = *dp3_Seed;
int rows = g->GetColumnVertexCount();
int cols = g->GetRightVertexColorCount();
cout<<"Right Seed matrix: ("<<rows<<","<<cols<<")"<<endl;
for(int i=0; i<rows; i++) {
for(int j=0; j<cols; j++) {
cout<<setw(6)<<RSeed[i][j];
}
cout<<endl;
}
//END SECTION
Pause();
//GraphColoringInterface * g = new GraphColoringInterface();
delete g;
g = NULL;
//double*** dp3_Seed = new double**;
delete dp3_Seed;
dp3_Seed = NULL;
RSeed = NULL;
//int *ip1_SeedRowCount = new int;
delete ip1_SeedRowCount;
ip1_SeedRowCount = NULL;
//int *ip1_SeedColumnCount = new int;
delete ip1_SeedColumnCount;
ip1_SeedColumnCount = NULL;
//unsigned int **uip2_HessianSparsityPattern = new unsigned int *[i_RowCount];//[5][5]
free_2DMatrix(uip2_JacobianSparsityPattern, i_RowCount);
uip2_JacobianSparsityPattern = NULL;
return 0;
}
int main()
{
// s_InputFile = baseDir + <name of the input file>
string s_InputFile; //path of the input file
s_InputFile = baseDir;
s_InputFile += DIR_SEPARATOR; s_InputFile += "Graphs"; s_InputFile += DIR_SEPARATOR; s_InputFile += "column-compress.mtx";
// Step 1: Determine sparsity structure of the Jacobian.
// This step is done by an AD tool. For the purpose of illustration here, we read the structure from a file,
// and store the structure in a Compressed Row Format.
unsigned int *** uip3_SparsityPattern = new unsigned int **; //uip3_ means triple pointers of type unsigned int
double*** dp3_Value = new double**; //dp3_ means triple pointers of type double. Other prefixes follow the same notation
int rowCount, columnCount;
ConvertMatrixMarketFormat2RowCompressedFormat(s_InputFile, uip3_SparsityPattern, dp3_Value,rowCount, columnCount);
cout<<"just for debugging purpose, display the 2 matrices: the matrix with SparsityPattern only and the matrix with Value"<<endl;
cout<<fixed<<showpoint<<setprecision(2); //formatting output
cout<<"(*uip3_SparsityPattern)"<<endl;
displayCompressedRowMatrix((*uip3_SparsityPattern),rowCount);
cout<<"(*dp3_Value)"<<endl;
displayCompressedRowMatrix((*dp3_Value),rowCount);
cout<<"Finish ConvertMatrixMarketFormat2RowCompressedFormat()"<<endl;
Pause();
//Step 2: Obtain the seed matrix via coloring.
double*** dp3_Seed = new double**;
int *ip1_SeedRowCount = new int;
int *ip1_SeedColumnCount = new int;
int *ip1_ColorCount = new int; //The number of distinct colors used to color the graph
//Step 2.1: Read the sparsity pattern of the given Jacobian matrix (compressed sparse rows format)
//and create the corresponding bipartite graph
BipartiteGraphPartialColoringInterface *g = new BipartiteGraphPartialColoringInterface(SRC_MEM_ADOLC, *uip3_SparsityPattern, rowCount, columnCount);
//Step 2.2: Do Partial-Distance-Two-Coloring the bipartite graph with the specified ordering
g->PartialDistanceTwoColoring("SMALLEST_LAST", "COLUMN_PARTIAL_DISTANCE_TWO");
//Step 2.3 (Option 1): From the coloring information, create and return the seed matrix
(*dp3_Seed) = g->GetSeedMatrix(ip1_SeedRowCount, ip1_SeedColumnCount);
/* Notes:
Step 2.3 (Option 2): From the coloring information, you can also get the vector of colorIDs of left or right vertices (depend on the s_ColoringVariant that you choose)
vector<int> vi_VertexPartialColors;
g->GetVertexPartialColors(vi_VertexPartialColors);
*/
cout<<"Finish GenerateSeed()"<<endl;
*ip1_ColorCount = *ip1_SeedColumnCount;
//Display results of step 2
printf(" dp3_Seed %d x %d \n", *ip1_SeedRowCount, *ip1_SeedColumnCount);
displayMatrix(*dp3_Seed, *ip1_SeedRowCount, *ip1_SeedColumnCount);
Pause();
// Step 3: Obtain the Jacobian-seed matrix product.
// This step will also be done by an AD tool. For the purpose of illustration here, the orginial matrix V
// (for Values) is multiplied with the seed matrix S. The resulting matrix is stored in dp3_CompressedMatrix.
double*** dp3_CompressedMatrix = new double**;
cout<<"Start MatrixMultiplication()"<<endl;
MatrixMultiplication_VxS(*uip3_SparsityPattern, *dp3_Value, rowCount, columnCount, *dp3_Seed, *ip1_ColorCount, dp3_CompressedMatrix);
cout<<"Finish MatrixMultiplication()"<<endl;
displayMatrix(*dp3_CompressedMatrix,rowCount,*ip1_ColorCount);
Pause();
//Step 4: Recover the numerical values of the original matrix from the compressed representation.
// The new values are store in "dp3_NewValue"
double*** dp3_NewValue = new double**;
JacobianRecovery1D* jr1d = new JacobianRecovery1D;
int rowCount_for_dp3_NewValue = jr1d->RecoverD2Cln_RowCompressedFormat_unmanaged(g, *dp3_CompressedMatrix, *uip3_SparsityPattern, dp3_NewValue);
/* RecoverD2Cln_RowCompressedFormat_unmanaged is called instead of RecoverD2Cln_RowCompressedFormat so that
we could manage the memory deallocation for dp3_NewValue by ourselves.
This way, we can reuse (*dp3_NewValue) to store new values if RecoverD2Cln_RowCompressedFormat...() need to be called again.
//*/
cout<<"Finish Recover()"<<endl;
displayCompressedRowMatrix(*dp3_NewValue,rowCount);
Pause();
//Check for consistency, make sure the values in the 2 matrices are the same.
if (CompressedRowMatricesAreEqual(*dp3_Value, *dp3_NewValue, rowCount,0)) cout<< "*dp3_Value == dp3_NewValue"<<endl;
else cout<< "*dp3_Value != dp3_NewValue"<<endl;
Pause();
/* Let say that we have new matrices with the same sparsity structure (only the values changed),
We can take advantage of the memory already allocated to (*dp3_NewValue) and use (*dp3_NewValue) to stored the new values
by calling RecoverD2Cln_RowCompressedFormat_usermem recovery function.
This function works in the same way as RecoverD2Cln_RowCompressedFormat_unmanaged except that the memory for (*dp3_NewValue)
is reused and therefore, takes less time than the _unmanaged counterpart.
//*/
for(int i=0; i<3;i++) {
jr1d->RecoverD2Cln_RowCompressedFormat_usermem(g, *dp3_CompressedMatrix, *uip3_SparsityPattern, dp3_NewValue);
}
//Deallocate memory for 2-dimensional array (*dp3_NewValue)
for(int i=0; i<rowCount_for_dp3_NewValue;i++) {
free((*dp3_NewValue)[i]);
}
free(*dp3_NewValue);
//Deallocate memory using functions in Utilities/MatrixDeallocation.h
free_2DMatrix(uip3_SparsityPattern, rowCount);
uip3_SparsityPattern=NULL;
free_2DMatrix(dp3_Value, rowCount);
dp3_Value=NULL;
delete dp3_Seed;
dp3_Seed = NULL;
delete ip1_SeedRowCount;
ip1_SeedRowCount=NULL;
delete ip1_SeedColumnCount;
ip1_SeedColumnCount = NULL;
free_2DMatrix(dp3_CompressedMatrix, rowCount);
dp3_CompressedMatrix = NULL;
delete ip1_ColorCount;
ip1_ColorCount = NULL;
delete jr1d;
jr1d = NULL;
delete dp3_NewValue;
dp3_NewValue=NULL;
delete g;
g=NULL;
return 0;
}
int main()
{
int i, j, k, l, sum, n, m, nnz, direct = 1, found;
double f;
double *x, *c;
adouble fad, *xad, *cad;
//double** Zpp;
//double** Zppf;
double** J;
//double* s;
//int p_H_dir, p_H_indir;
size_t tape_stats[11];
int num;
FILE *fp_JP;
double **Seed_J;
double **Jc;
int p_J;
int recover = 1;
int jac_vec = 1;
int compute_full = 1;
int output_sparsity_pattern_J = 0;
//int output_sparsity_pattern_H = 1;
//int use_direct_method = 1;
//int output_direct = 0;
//int use_indirect_method = 1;
//int output_indirect = 0;
double t_f_1, t_f_2, div_f=0, div_c=0, div_JP=0, div_JP2=0, div_Seed=0, div_Seed_C=0, div_Jc=0, div_Jc_C=0, div_rec=0, div_rec_C=0, div_J=0;
//double test;
unsigned int *rind;
unsigned int *cind;
double *values;
//tring s_InputFile = "test_mat.mtx";
//string s_InputFile = "jac_pat.mtx";
//------------------------------------------------------------------------------------
// problem definition + evaluation
init_dim(&n,&m); // initialize n and m
printf(" n = %d m = %d\n",n,m);
x = (double*) malloc(n*sizeof(double)); // x: vector input for function evaluation
c = (double*) malloc(m*sizeof(double)); // c: constraint vector
cad = new adouble[m];
init_startpoint(x,n);
t_f_1 = k_getTime();
for(i=0;i<repnum;i++)
f = feval(x,n);
t_f_2 = k_getTime();
div_f = (t_f_2 - t_f_1)*1.0/repnum;
printf("XXX The time needed for function evaluation: %10.6f \n \n", div_f);
t_f_1 = k_getTime();
for(i=0;i<repnum;i++)
ceval(x,c,n);
t_f_2 = k_getTime();
div_c = (t_f_2 - t_f_1)*1.0/repnum;
printf("XXX The time needed for constraint evaluation: %10.6f \n \n", div_c);
trace_on(tag_f);
fad = feval_ad(x, n); // feval_ad: derivative of feval
fad >>= f;
trace_off();
trace_on(tag_c);
ceval_ad(x, cad, n); //ceval_ad: derivative of ceval
for(i=0;i<m;i++)
cad[i] >>= f;
trace_off();
//return 1;
tapestats(tag_c,tape_stats); // reading of tape statistics
printf("\n independents %ld\n",(long)tape_stats[0]);
printf(" dependents %ld\n",(long)tape_stats[1]);
printf(" operations %ld\n",(long)tape_stats[5]);
printf(" buffer size %ld\n",(long)tape_stats[4]);
printf(" maxlive %ld\n",(long)tape_stats[2]);
printf(" valstack size %ld\n\n",(long)tape_stats[3]);
//------------------------------------------------------------------------------------
// full Jacobian:
div_J = -1;
if(compute_full == 1)
{
J = myalloc2(m,n);
t_f_1 = k_getTime();
jacobian(tag_c,m,n,x,J);
t_f_2 = k_getTime();
div_J = (t_f_2 - t_f_1);
printf("XXX The time needed for full Jacobian: %10.6f \n \n", div_J);
printf("XXX runtime ratio: %10.6f \n \n", div_J/div_c);
//save the matrix into a file (non-zero entries only)
fp_JP = fopen("jac_full.mtx","w");
fprintf(fp_JP,"%d %d\n",m,n);
for (i=0;i<m;i++)
{
for (j=0;j<n;j++)
if(J[i][j]!=0.0) fprintf(fp_JP,"%d %d %10.6f\n",i,j,J[i][j] );
}
fclose(fp_JP);
}
//------------------------------------------------------------------------------------
printf("XXX THE 4 STEP TO COMPUTE SPARSE MATRICES USING ColPack \n \n");
// STEP 1: Determination of sparsity pattern of Jacobian JP:
unsigned int *rb=NULL; /* dependent variables */
unsigned int *cb=NULL; /* independent variables */
unsigned int **JP=NULL; /* compressed block row storage */
int ctrl[2];
JP = (unsigned int **) malloc(m*sizeof(unsigned int*));
ctrl[0] = 0; ctrl[1] = 0;
t_f_1 = k_getTime();
jac_pat(tag_c, m, n, x, JP, ctrl); //ADOL-C calculate the sparsity pattern
t_f_2 = k_getTime();
div_JP = (t_f_2 - t_f_1);
printf("XXX STEP 1: The time needed for Jacobian pattern: %10.6f \n \n", div_JP);
printf("XXX STEP 1: runtime ratio: %10.6f \n \n", div_JP/div_c);
nnz = 0;
for (i=0;i<m;i++)
nnz += JP[i][0];
printf(" nnz %d \n",nnz);
printf(" hier 1a\n");
//------------------------------------------------------------------------------------
// STEP 2: Determination of Seed matrix:
double tg_C;
int dummy;
t_f_1 = k_getTime();
BipartiteGraphPartialColoringInterface * gGraph = new BipartiteGraphPartialColoringInterface(SRC_MEM_ADOLC, JP, m, n);
//gGraph->PrintBipartiteGraph();
t_f_2 = k_getTime();
printf("XXX STEP 2: The time needed for Graph construction: %10.6f \n \n", (t_f_2-t_f_1) );
printf("XXX STEP 2: runtime ratio: %10.6f \n \n", (t_f_2-t_f_1)/div_c);
t_f_1 = k_getTime();
//gGraph->GenerateSeedJacobian(&Seed_J, &dummy, &p_J,
// "NATURAL", "COLUMN_PARTIAL_DISTANCE_TWO");
gGraph->PartialDistanceTwoColoring("NATURAL", "COLUMN_PARTIAL_DISTANCE_TWO");
t_f_2 = k_getTime();
printf("XXX STEP 2: The time needed for Coloring: %10.6f \n \n", (t_f_2-t_f_1));
printf("XXX STEP 2: runtime ratio: %10.6f \n \n", (t_f_2-t_f_1)/div_c);
t_f_1 = k_getTime();
Seed_J = gGraph->GetSeedMatrix(&dummy, &p_J);
t_f_2 = k_getTime();
tg_C = t_f_2 - t_f_1;
printf("XXX STEP 2: The time needed for Seed generation: %10.6f \n \n", tg_C);
printf("XXX STEP 2: runtime ratio: %10.6f \n \n", tg_C/div_c);
//*/
//------------------------------------------------------------------------------------
// STEP 3: Jacobian-matrix product:
// ADOL-C:
//*
if (jac_vec == 1)
{
Jc = myalloc2(m,p_J);
t_f_1 = k_getTime();
printf(" hier 1\n");
fov_forward(tag_c,m,n,p_J,x,Seed_J,c,Jc);
printf(" hier 2\n");
t_f_2 = k_getTime();
div_Jc = (t_f_2 - t_f_1);
printf("XXX STEP 3: The time needed for Jacobian-matrix product: %10.6f \n \n", div_Jc);
printf("XXX STEP 3: runtime ratio: %10.6f \n \n", div_Jc/div_c);
}
//------------------------------------------------------------------------------------
// STEP 4: computed Jacobians/ recovery
if (recover == 1)
{
JacobianRecovery1D jr1d;
printf("m = %d, n = %d, p_J = %d \n",m,n,p_J);
//printmatint_c("JP Jacobian Pattern",m,JP);
//printmat("Jc Jacobian compressed",m,p_J,Jc);
t_f_1 = k_getTime();
jr1d.RecoverD2Cln_CoordinateFormat (gGraph, Jc, JP, &rind, &cind, &values);
t_f_2 = k_getTime();
div_rec_C = (t_f_2 - t_f_1);
printf("XXX STEP 4: The time needed for Recovery: %10.6f \n \n", div_rec_C);
printf("XXX STEP 4: runtime ratio: %10.6f \n \n", div_rec_C/div_c);
//save recovered matrix into file
fp_JP = fopen("jac_recovered.mtx","w");
fprintf(fp_JP,"%d %d %d\n",m,n,nnz);
for (i=0;i<nnz;i++)
{
fprintf(fp_JP,"%d %d %10.6f\n",rind[i],cind[i],values[i] );
}
fclose(fp_JP);
}
/*By this time, if you compare the 2 output files: jac_full.mtx and jac_recovered.mtx
You should be able to see that the non-zero entries are identical
*/
free(JP);
delete[] cad;
free(c);
free(x);
delete gGraph;
if(jac_vec == 1) {
myfree2(Jc);
}
if(compute_full == 1)
{
myfree2(J);
}
return 0;
}
int main()
{
// s_InputFile = baseDir + <name of the input file>
string s_InputFile; //path of the input file
s_InputFile = baseDir;
s_InputFile += DIR_SEPARATOR; s_InputFile += "Graphs"; s_InputFile += DIR_SEPARATOR; s_InputFile += "column-compress.mtx";
//s_InputFile += DIR_SEPARATOR; s_InputFile += "Graphs"; s_InputFile += DIR_SEPARATOR; s_InputFile += "hess_pat.mtx";
// Step 1: Determine sparsity structure of the Jacobian.
// This step is done by an AD tool. For the purpose of illustration here, we read the structure from a file,
// and store the structure in a Compressed Row Format and then ADIC format.
unsigned int *** uip3_SparsityPattern = new unsigned int **; //uip3_ means triple pointers of type unsigned int
double*** dp3_Value = new double**; //dp3_ means triple pointers of type double. Other prefixes follow the same notation
int rowCount, columnCount;
ConvertMatrixMarketFormat2RowCompressedFormat(s_InputFile, uip3_SparsityPattern, dp3_Value,rowCount, columnCount);
cout<<"just for debugging purpose, display the 2 matrices: the matrix with SparsityPattern only and the matrix with Value"<<endl;
cout<<"Matrix rowCount = "<<rowCount<<"; columnCount = "<<columnCount<<endl;
cout<<fixed<<showpoint<<setprecision(2); //formatting output
cout<<"(*uip3_SparsityPattern)"<<endl;
displayCompressedRowMatrix((*uip3_SparsityPattern),rowCount, true);
cout<<"(*dp3_Value)"<<endl;
displayCompressedRowMatrix((*dp3_Value),rowCount);
cout<<"Finish ConvertMatrixMarketFormat2RowCompressedFormat()"<<endl;
Pause();
std::list<std::set<int> > lsi_SparsityPattern;
std::list<std::vector<double> > lvd_Value;
ConvertRowCompressedFormat2ADIC( (*uip3_SparsityPattern) , rowCount, (*dp3_Value), lsi_SparsityPattern, lvd_Value);
cout<<"just for debugging purpose, display the matrix in ADIC format rowCount = "<<rowCount<<endl;
cout<<"Display lsi_SparsityPattern"<<endl;
DisplayADICFormat_Sparsity(lsi_SparsityPattern);
cout<<"Display lvd_Value"<<endl;
DisplayADICFormat_Value(lvd_Value);
cout<<"Finish ConvertRowCompressedFormat2CSR()"<<endl;
Pause();
//Step 2: Coloring.
int *ip1_ColorCount = new int; //The number of distinct colors used to color the graph
//Step 2.1: Read the sparsity pattern of the given Jacobian matrix (ADIC format)
//and create the corresponding bipartite graph
BipartiteGraphPartialColoringInterface *g = new BipartiteGraphPartialColoringInterface(SRC_MEM_ADIC, &lsi_SparsityPattern, columnCount);
//Step 2.2: Do Partial-Distance-Two-Coloring the bipartite graph with the specified ordering
g->PartialDistanceTwoColoring("SMALLEST_LAST", "COLUMN_PARTIAL_DISTANCE_TWO");
//Step 2.3: From the coloring information, you can get the vector of colorIDs of left or right vertices (depend on the s_ColoringVariant that you choose)
vector<int> vi_VertexPartialColors;
g->GetVertexPartialColors(vi_VertexPartialColors);
*ip1_ColorCount = g->GetRightVertexColorCount();
cout<<"Finish GetVertexPartialColors()"<<endl;
//Display results of step 2
printf(" Display vi_VertexPartialColors *ip1_ColorCount=%d \n",*ip1_ColorCount);
displayVector(vi_VertexPartialColors);
Pause();
// Step 3: Obtain the Jacobian-seed matrix product.
// This step will also be done by an AD tool. For the purpose of illustration here, the orginial matrix V
// (for Values) is multiplied with the seed matrix S (represented as a vector of colors vi_VertexPartialColors).
// The resulting matrix is stored in dp3_CompressedMatrix.
double*** dp3_CompressedMatrix = new double**;
cout<<"Start MatrixMultiplication()"<<endl;
MatrixMultiplication_VxS__usingVertexPartialColors(lsi_SparsityPattern, lvd_Value, columnCount, vi_VertexPartialColors, *ip1_ColorCount, dp3_CompressedMatrix);
cout<<"Finish MatrixMultiplication()"<<endl;
displayMatrix(*dp3_CompressedMatrix,rowCount,*ip1_ColorCount);
Pause();
//Step 4: Recover the numerical values of the original matrix from the compressed representation.
// The new values are store in "lvd_NewValue"
std::list<std::vector<double> > lvd_NewValue;
JacobianRecovery1D* jr1d = new JacobianRecovery1D;
jr1d->RecoverD2Cln_ADICFormat(g, *dp3_CompressedMatrix, lsi_SparsityPattern, lvd_NewValue);
cout<<"Finish Recover()"<<endl;
DisplayADICFormat_Value(lvd_NewValue);
Pause();
//Check for consistency, make sure the values in the 2 matrices are the same.
if (ADICMatricesAreEqual(lvd_Value, lvd_NewValue,0)) cout<< "lvd_Value == lvd_NewValue"<<endl;
else cout<< "lvd_Value != lvd_NewValue"<<endl;
Pause();
//Deallocate memory using functions in Utilities/MatrixDeallocation.h
free_2DMatrix(uip3_SparsityPattern, rowCount);
uip3_SparsityPattern=NULL;
free_2DMatrix(dp3_Value, rowCount);
dp3_Value=NULL;
free_2DMatrix(dp3_CompressedMatrix, rowCount);
dp3_CompressedMatrix = NULL;
delete ip1_ColorCount;
ip1_ColorCount = NULL;
delete jr1d;
jr1d = NULL;
delete g;
g=NULL;
return 0;
}
int main()
{
// s_InputFile = baseDir + <name of the input file>
string s_InputFile; //path of the input file
s_InputFile = baseDir;
s_InputFile += DIR_SEPARATOR; s_InputFile += "Graphs"; s_InputFile += DIR_SEPARATOR; s_InputFile += "column-compress.mtx";
// Step 1: Determine sparsity structure of the Jacobian.
// This step is done by an AD tool. For the purpose of illustration here, we read the structure from a file,
// and store the structure in a Compressed Row Format.
unsigned int *** uip3_SparsityPattern = new unsigned int **; //uip3_ means triple pointers of type unsigned int
double*** dp3_Value = new double**; //dp3_ means triple pointers of type double. Other prefixes follow the same notation
int rowCount, columnCount;
ConvertMatrixMarketFormat2RowCompressedFormat(s_InputFile, uip3_SparsityPattern, dp3_Value,rowCount, columnCount);
cout<<"just for debugging purpose, display the 2 matrices: the matrix with SparsityPattern only and the matrix with Value"<<endl;
cout<<fixed<<showpoint<<setprecision(2); //formatting output
cout<<"(*uip3_SparsityPattern)"<<endl;
displayCompressedRowMatrix((*uip3_SparsityPattern),rowCount);
cout<<"(*dp3_Value)"<<endl;
displayCompressedRowMatrix((*dp3_Value),rowCount);
cout<<"Finish ConvertMatrixMarketFormat2RowCompressedFormat()"<<endl;
Pause();
//Step 2: Obtain the seed matrix via coloring.
double*** dp3_Seed = new double**;
int *ip1_SeedRowCount = new int;
int *ip1_SeedColumnCount = new int;
int *ip1_ColorCount = new int; //The number of distinct colors used to color the graph
//Step 2.1: Read the sparsity pattern of the given Jacobian matrix (compressed sparse rows format)
//and create the corresponding bipartite graph
BipartiteGraphPartialColoringInterface *g = new BipartiteGraphPartialColoringInterface(SRC_MEM_ADOLC, *uip3_SparsityPattern, rowCount, columnCount);
//Step 2.2: Do Partial-Distance-Two-Coloring the bipartite graph with the specified ordering
g->PartialDistanceTwoColoring( "SMALLEST_LAST", "COLUMN_PARTIAL_DISTANCE_TWO");
//Step 2.3 (Option 1): From the coloring information, create and return the seed matrix
(*dp3_Seed) = g->GetSeedMatrix(ip1_SeedRowCount, ip1_SeedColumnCount);
/* Notes:
Step 2.3 (Option 2): From the coloring information, you can also get the vector of colorIDs of left or right vertices (depend on the s_ColoringVariant that you choose)
vector<int> vi_VertexPartialColors;
g->GetVertexPartialColors(vi_VertexPartialColors);
*/
cout<<"Finish GenerateSeed()"<<endl;
*ip1_ColorCount = *ip1_SeedColumnCount;
//Display results of step 2
printf(" dp3_Seed %d x %d \n", *ip1_SeedRowCount, *ip1_SeedColumnCount);
displayMatrix(*dp3_Seed, *ip1_SeedRowCount, *ip1_SeedColumnCount);
Pause();
// Step 3: Obtain the Jacobian-seed matrix product.
// This step will also be done by an AD tool. For the purpose of illustration here, the orginial matrix V
// (for Values) is multiplied with the seed matrix S. The resulting matrix is stored in dp3_CompressedMatrix.
double*** dp3_CompressedMatrix = new double**;
cout<<"Start MatrixMultiplication()"<<endl;
MatrixMultiplication_VxS(*uip3_SparsityPattern, *dp3_Value, rowCount, columnCount, *dp3_Seed, *ip1_ColorCount, dp3_CompressedMatrix);
cout<<"Finish MatrixMultiplication()"<<endl;
displayMatrix(*dp3_CompressedMatrix,rowCount,*ip1_ColorCount);
Pause();
//Step 4: Recover the numerical values of the original matrix from the compressed representation.
// The new values are store in "dp2_JacobianValue"
unsigned int** ip2_RowIndex = new unsigned int*;
unsigned int** ip2_ColumnIndex = new unsigned int*;
double** dp2_JacobianValue = new double*;
JacobianRecovery1D* jr1d = new JacobianRecovery1D;
jr1d->RecoverD2Cln_SparseSolversFormat(g, *dp3_CompressedMatrix, *uip3_SparsityPattern, ip2_RowIndex, ip2_ColumnIndex, dp2_JacobianValue);
cout<<"Finish Recover()"<<endl;
cout<<endl<<"Display result, the structure and values should be similar to the original one"<<endl;
cout<<"Display *ip2_RowIndex"<<endl;
displayVector(*ip2_RowIndex,g->GetRowVertexCount()+1);
cout<<"Display *ip2_ColumnIndex"<<endl;
displayVector(*ip2_ColumnIndex, (*ip2_RowIndex)[g->GetRowVertexCount()]-1);
cout<<"Display *dp2_JacobianValue"<<endl;
displayVector(*dp2_JacobianValue, (*ip2_RowIndex)[g->GetRowVertexCount()]-1);
Pause();
//Deallocate memory using functions in Utilities/MatrixDeallocation.h
free_2DMatrix(uip3_SparsityPattern, rowCount);
uip3_SparsityPattern=NULL;
free_2DMatrix(dp3_Value, rowCount);
dp3_Value=NULL;
delete dp3_Seed;
dp3_Seed = NULL;
delete ip1_SeedRowCount;
ip1_SeedRowCount=NULL;
delete ip1_SeedColumnCount;
ip1_SeedColumnCount = NULL;
free_2DMatrix(dp3_CompressedMatrix, rowCount);
dp3_CompressedMatrix = NULL;
delete ip1_ColorCount;
ip1_ColorCount = NULL;
delete jr1d;
jr1d = NULL;
delete ip2_RowIndex;
delete ip2_ColumnIndex;
delete dp2_JacobianValue;
ip2_RowIndex=NULL;
ip2_ColumnIndex=NULL;
dp2_JacobianValue=NULL;
delete g;
g=NULL;
return 0;
}
