void test3() {
Matrix m1 = matrix_create( 3, 3 );
int i,j;
for ( i=0; i<3; ++i ) { for ( j=0; j<3; ++j ) { m1[i][j] = i+2*j; } }
printf( "m1 =\n" );
matrix_print( m1, 3, 3 );
printf( "delete row 0 from m1 =\n" );
matrix_delete_row( m1, 0, 3 );
matrix_print( m1, 2, 3 );
matrix_delete( m1, 2 );
}
void test4() {
Matrix m1 = matrix_create( 3, 3 );
int i,j;
for ( i=0; i<3; ++i ) { for ( j=0; j<3; ++j ) { m1[i][j] = i+2*j; } }
printf( "m1 =\n" );
matrix_print( m1, 3, 3 );
printf( "delete column 1 from m1 =\n" );
matrix_delete_column( m1, 1, 3, 3 );
matrix_print( m1, 3, 2 );
matrix_delete( m1, 3 );
}
/* calculate percent error between A and B
in terms of Frobenius norm: 100*norm(A - B)/norm(A) */
double get_percent_error_between_two_mats(mat *A, mat *B){
int m,n;
double normA, normB, normA_minus_B;
mat *A_minus_B;
m = A->nrows;
n = A->ncols;
A_minus_B = matrix_new(m,n);
matrix_copy(A_minus_B, A);
matrix_sub(A_minus_B, B);
normA = get_matrix_frobenius_norm(A);
normB = get_matrix_frobenius_norm(B);
normA_minus_B = get_matrix_frobenius_norm(A_minus_B);
matrix_delete(A_minus_B);
return 100.0*normA_minus_B/normA;
}
static void allpairs_compare_SWALIGN( const char *name1, const char *data1, int size1, const char *name2, const char *data2, int size2 )
{
char *stra = strdup(data1);
char *strb = strdup(data2);
stra[size1-1] = 0;
strb[size2-1] = 0;
struct matrix *m = matrix_create(size1-1,size2-1);
struct alignment *aln = align_smith_waterman(m,stra,strb);
pthread_mutex_lock(&mutex);
printf("> %s %s\n",name1,name2);
alignment_print(stdout,stra,strb,aln);
pthread_mutex_unlock(&mutex);
free(stra);
free(strb);
matrix_delete(m);
alignment_delete(aln);
}
void ekf_delete(struct ekf *ekf)
{
if (ekf) {
if (ekf->X)
matrix_delete(ekf->X);
if (ekf->P)
matrix_delete(ekf->P);
if (ekf->Q)
matrix_delete(ekf->Q);
if (ekf->R)
matrix_delete(ekf->R);
if (ekf->A)
matrix_delete(ekf->A);
if (ekf->H)
matrix_delete(ekf->H);
if (ekf->K)
matrix_delete(ekf->K);
free(ekf);
}
}
void estimate_rank_and_buildQ2(mat *M, int kblock, double TOL, mat **Y, mat **Q, int *good_rank){
int m,n,i,j,ind,exit_loop = 0;
double error_norm;
mat *RN,*Y_new,*Y_big,*QtM,*QQtM;
vec *vi,*vj,*p,*p1;
m = M->nrows;
n = M->ncols;
// build random matrix
printf("form RN..\n");
RN = matrix_new(n,kblock);
initialize_random_matrix(RN);
// multiply to get matrix of random samples Y
printf("form Y: %d x %d..\n",m,kblock);
*Y = matrix_new(m, kblock);
matrix_matrix_mult(M, RN, *Y);
ind = 0;
while(!exit_loop){
printf("form Q..\n");
if(ind > 0){
matrix_delete(*Q);
}
*Q = matrix_new((*Y)->nrows, (*Y)->ncols);
QR_factorization_getQ(*Y, *Q);
// compute QtM
QtM = matrix_new((*Q)->ncols, M->ncols);
matrix_transpose_matrix_mult(*Q,M,QtM);
// compute QQtM
QQtM = matrix_new(M->nrows, M->ncols);
matrix_matrix_mult(*Q,QtM,QQtM);
error_norm = 0.01*get_percent_error_between_two_mats(QQtM, M);
printf("Y is of size %d x %d and error_norm = %f\n", (*Y)->nrows, (*Y)->ncols, error_norm);
*good_rank = (*Y)->ncols;
// add more samples if needed
if(error_norm > TOL){
Y_new = matrix_new(m, kblock);
initialize_random_matrix(RN);
matrix_matrix_mult(M, RN, Y_new);
Y_big = matrix_new((*Y)->nrows, (*Y)->ncols + Y_new->ncols);
append_matrices_horizontally(*Y, Y_new, Y_big);
matrix_delete(*Y);
*Y = matrix_new(Y_big->nrows,Y_big->ncols);
matrix_copy(*Y,Y_big);
matrix_delete(Y_big);
matrix_delete(Y_new);
matrix_delete(QtM);
matrix_delete(QQtM);
ind++;
}
else{
matrix_delete(RN);
exit_loop = 1;
}
}
}
void estimate_rank_and_buildQ(mat *M, double frac_of_max_rank, double TOL, mat **Q, int *good_rank){
int m,n,i,j,ind,maxdim;
double vec_norm;
mat *RN,*Y,*Qbig,*Qsmall;
vec *vi,*vj,*p,*p1;
m = M->nrows;
n = M->ncols;
maxdim = round(min(m,n)*frac_of_max_rank);
vi = vector_new(m);
vj = vector_new(m);
p = vector_new(m);
p1 = vector_new(m);
// build random matrix
printf("form RN..\n");
RN = matrix_new(n, maxdim);
initialize_random_matrix(RN);
// multiply to get matrix of random samples Y
printf("form Y: %d x %d..\n",m,maxdim);
Y = matrix_new(m, maxdim);
matrix_matrix_mult(M, RN, Y);
// estimate rank k and build Q from Y
printf("form Qbig..\n");
Qbig = matrix_new(m, maxdim);
matrix_copy(Qbig, Y);
printf("estimate rank with TOL = %f..\n", TOL);
*good_rank = maxdim;
int forbreak = 0;
for(j=0; !forbreak && j<maxdim; j++){
matrix_get_col(Qbig, j, vj);
for(i=0; i<j; i++){
matrix_get_col(Qbig, i, vi);
project_vector(vj, vi, p);
vector_sub(vj, p);
if(vector_get2norm(p) < TOL && vector_get2norm(p1) < TOL){
*good_rank = j;
forbreak = 1;
break;
}
vector_copy(p1,p);
}
vec_norm = vector_get2norm(vj);
vector_scale(vj, 1.0/vec_norm);
matrix_set_col(Qbig, j, vj);
}
printf("estimated rank = %d\n", *good_rank);
Qsmall = matrix_new(m, *good_rank);
*Q = matrix_new(m, *good_rank);
matrix_copy_first_columns(Qsmall, Qbig);
QR_factorization_getQ(Qsmall, *Q);
matrix_delete(RN);
matrix_delete(Y);
matrix_delete(Qsmall);
matrix_delete(Qbig);
}
static void ac_update_curve_polygon(struct approximation_curve* ac)
{
Grille_flottant* m;
Grille_flottant* mt;
Grille_flottant* mmt;
Table_flottant* p;
Table_flottant* a;
Table_flottant* mp;
Table_flottant* params;
// Allocation de la table des points de contrôle de la courbe d'approximation
if (ac->curve_polygon.nb != (ac->degree + 1))
{
free(ac->curve_polygon.table);
ac->curve_polygon.nb = ac->degree + 1;
ALLOUER(ac->curve_polygon.table, ac->curve_polygon.nb);
}
// Paramétrisation de la courbe d'approximation
if (ac->use_uniform_parameterization)
params = ac_uniform_parameterization(&ac->points);
else
params = ac_non_uniform_parameterization(&ac->points);
// Construction de la matrice de Bernstein, de sa transposée et de leur produit
m = matrix_create(ac->degree + 1, ac->points.nb);
ac_bernstein_matrix(m, params);
mt = matrix_transpose(m);
mmt = matrix_product(m, mt);
// Résolution des 3 systèmes d'équations (composantes x, y et z) des points de contrôle recherchés
p = malloc_table_flottant(ac->points.nb);
a = malloc_table_flottant(ac->degree + 1);
mp = malloc_table_flottant(m->nb_lignes);
// X
get_triplets_x_values(&ac->points, p);
matrix_vector_product(m, p, mp);
if (resolution_systeme_lineaire(mmt, mp, a))
EXIT;
set_triplets_x_values(&ac->curve_polygon, a);
// Y
get_triplets_y_values(&ac->points, p);
matrix_vector_product(m, p, mp);
if (resolution_systeme_lineaire(mmt, mp, a))
EXIT;
set_triplets_y_values(&ac->curve_polygon, a);
// Z
get_triplets_z_values(&ac->points, p);
matrix_vector_product(m, p, mp);
if (resolution_systeme_lineaire(mmt, mp, a))
EXIT;
set_triplets_z_values(&ac->curve_polygon, a);
// Libération des ressources
matrix_delete(m);
matrix_delete(mt);
matrix_delete(mmt);
free_table_flottant(a);
free_table_flottant(p);
free_table_flottant(mp);
free_table_flottant(params);
}
int main()
{
int i, j, m, n, k, p, q, s, vnum, offset;
int64_t frank, numnnz;
double val,normM,normU,normS,normV,normP,percent_error;
mat *M, *U, *S, *V, *P;
struct timeval start_time, end_time;
m = 50000;
n = 100000;
numnnz = m*n; // dense
M = matrix_new(m,n);
m = M->nrows;
n = M->ncols;
printf("sizes of M are %d by %d\n", m, n);
printf("m*n = %" PRId64 "\n", numnnz); // this is bigger than a 32 bit int can hold
printf("initializing random matrix..\n");
gettimeofday(&start_time,NULL);
initialize_random_matrix(M);
gettimeofday(&end_time,NULL);
printf("done loading..\n");
printf("elapsed time: about %4.2f seconds\n", get_seconds_frac(start_time,end_time));
// now test low rank SVD of M..
k = 1000; // rank we want
p = 20; // oversampling
q = 3; // power scheme
s = 1; // re-rotho for power scheme
vnum = 1; // scheme to use
printf("calling random SVD..\n");
gettimeofday(&start_time,NULL);
low_rank_svd_rand_decomp_fixed_rank(M, k, p, vnum, q, s, &frank, &U, &S, &V);
gettimeofday(&end_time,NULL);
printf("elapsed time: about %4.2f seconds\n", get_seconds_frac(start_time,end_time));
// form product matrix
P = matrix_new(m,n);
form_svd_product_matrix(U,S,V,P);
// get norms of each
normM = get_matrix_frobenius_norm(M);
normU = get_matrix_frobenius_norm(U);
normS = get_matrix_frobenius_norm(S);
normV = get_matrix_frobenius_norm(V);
normP = get_matrix_frobenius_norm(P);
printf("normM = %f ; normU = %f ; normS = %f ; normV = %f ; normP = %f\n", normM, normU, normS, normV, normP);
// calculate percent error
percent_error = get_percent_error_between_two_mats(M,P);
printf("percent_error between M and U S V^T = %f\n", percent_error);
// delete and exit
printf("delete and exit..\n");
matrix_delete(M);
matrix_delete(U);
matrix_delete(S);
matrix_delete(V);
matrix_delete(P);
return 0;
}
int main(int argc, char ** argv)
{
FILE * input;
struct seq *s1=0, *s2=0;
char ori;
char c;
int fileindex;
int del_input=0;
while((c = getopt(argc, argv, "a:o:k:m:q:xd:vh")) != (char) -1) {
switch (c) {
case 'a':
align_type = optarg;
break;
case 'o':
output_format = optarg;
break;
case 'm':
min_align = atoi(optarg);
break;
case 'q':
min_qual = atof(optarg);
break;
case 'x':
del_input = 1;
break;
case 'd':
debug_flags_set(optarg);
break;
case 'v':
cctools_version_print(stdout, argv[0]);
exit(0);
break;
default:
case 'h':
show_help(argv[0]);
exit(0);
break;
}
}
cctools_version_debug(D_DEBUG, argv[0]);
fileindex = optind;
if ((argc - optind) == 1) {
input = fopen(argv[fileindex], "r");
if (!input) {
fprintf(stderr, "sand_align_kernel: couldn't open %s: %s\n",argv[fileindex],strerror(errno));
exit(1);
}
} else {
input = stdin;
}
struct cseq *c1, *c2;
if(!strcmp(output_format,"ovl") || !strcmp(output_format, "ovl_new")) {
overlap_write_begin(stdout);
}
// outer loop: read first sequence in comparison list
while((c1=cseq_read(input))) {
s1 = cseq_uncompress(c1);
cseq_free(c1);
// inner loop: read sequences until null (indicating end of list)
// then continue again with outer loop. (two nulls to halt.)
while((c2=cseq_read(input))) {
s2 = cseq_uncompress(c2);
cseq_free(c2);
int dir = 0;
int start1 = 0;
int start2 = 0;
char* tmp = strdup(s2->metadata);
int metadata_valid = 0;
char* token = strtok(tmp, " ");
start2 = atoi(token);
metadata_valid++;
while((token = strtok(NULL, " ")))
{
dir = start1;
start1 = start2;
start2 = atoi(token);
metadata_valid++;
}
if(metadata_valid>=1 && dir==-1) {
seq_reverse_complement(s2);
ori = 'I';
} else {
ori = 'N';
}
struct matrix *m = matrix_create(s1->num_bases,s2->num_bases);
if(!m) {
fprintf(stderr,"sand_align_kernel: out of memory when creating alignment matrix.\n");
exit(1);
}
struct alignment *aln;
if(!strcmp(align_type,"sw")) {
aln = align_smith_waterman(m,s1->data,s2->data);
} else if(!strcmp(align_type,"ps")) {
aln = align_prefix_suffix(m,s1->data,s2->data, min_align);
} else if(!strcmp(align_type,"banded")) {
if(metadata_valid<3) {
fprintf(stderr,"sand_align_kernel: sequence %s did not indicate start positions for the banded alignment.\n",s2->name);
exit(1);
}
/* The width of the band is proportional to the desired quality of the match. */
int k = 2 + min_qual * MIN(s1->num_bases,s2->num_bases) / 2.0;
if(k<5) k = 5;
aln = align_banded(m,s1->data, s2->data, start1, start2, k);
} else {
fprintf(stderr,"unknown alignment type: %s\n",align_type);
exit(1);
}
aln->ori = ori;
if(aln->quality <= min_qual) {
if(!strcmp(output_format,"ovl")) {
overlap_write_v5(stdout, aln, s1->name, s2->name);
} else if(!strcmp(output_format, "ovl_new")) {
overlap_write_v7(stdout, aln, s1->name, s2->name);
} else if(!strcmp(output_format,"matrix")) {
printf("*** %s alignment of sequences %s and %s (quality %lf):\n\n",align_type,s1->name,s2->name,aln->quality);
matrix_print(m,s1->data,s2->data);
} else if(!strcmp(output_format,"align")) {
printf("*** %s alignment of sequences %s and %s (quality %lf):\n\n",align_type,s1->name,s2->name,aln->quality);
alignment_print(stdout,s1->data,s2->data,aln);
} else {
printf("unknown output format '%s'\n",output_format);
exit(1);
}
}
matrix_delete(m);
seq_free(s2);
alignment_delete(aln);
}
seq_free(s1);
}
fclose(input);
if(!strcmp(output_format,"ovl") || !strcmp(output_format, "ovl_new")) {
overlap_write_end(stdout);
}
if ((argc - optind) == 1 && del_input == 1)
{
remove(argv[fileindex]);
}
return 0;
}
mat matrix_solve_qr(mat xMatrix, mat yMatrix) {
mat qr,x,result;
double *rDiag,s;
int i,n,m,k,j,nx;
/* Initial validations */
if (xMatrix->m != yMatrix->m) {
printf("Matrix row dimensions must agree.\n");
exit(1);
}
/* Perform a QR decomp. */
n = xMatrix->n;
m = xMatrix->m;
rDiag = (double*)calloc(n,sizeof(double));
qr = matrix_copy(xMatrix);
// Main loop.
for (k = 0; k < n; k++) {
// Compute 2-norm of k-th column without under/overflow.
double nrm = 0;
for (i = k; i < m; i++) {
nrm = calc_hypot(nrm, qr->v[i][k]);
}
if (nrm != 0.0) {
// Form k-th Householder vector.
if (qr->v[k][k] < 0) {
nrm = -nrm;
}
for (i = k; i < m; i++) {
qr->v[i][k] /= nrm;
}
qr->v[k][k] += 1.0;
// Apply transformation to remaining columns.
for (j = k + 1; j < n; j++) {
s = 0.0;
for (i = k; i < m; i++) {
s += qr->v[i][k] * qr->v[i][j];
}
s = -s / qr->v[k][k];
for (i = k; i < m; i++) {
qr->v[i][j] += s * qr->v[i][k];
}
}
}
rDiag[k] = -nrm;
}
/* Validate */
for(i=0;i<n;i++) {
if( fabs(rDiag[i])==0 ) {
printf("Matrix is rank deficient. Data fails to converge.");
exit(1);
}
}
/* Now solve */
/* Copy right hand side */
nx = yMatrix->n;
x = matrix_copy(yMatrix);
/* Compute Y = transpose(Q)*B */
for (k = 0; k < n; k++) {
for (j = 0; j < nx; j++) {
s = 0.0;
for (i = k; i < m; i++) {
s += qr->v[i][k] * x->v[i][j];
}
s = -s / qr->v[k][k];
for (i = k; i < m; i++) {
x->v[i][j] += s * qr->v[i][k];
}
}
}
/* Solve R*X = Y; */
for (k = n - 1; k >= 0; k--) {
for (j = 0; j < nx; j++) {
x->v[k][j] /= rDiag[k];
}
for (i = 0; i < k; i++) {
for (j = 0; j < nx; j++) {
x->v[i][j] -= x->v[k][j] * qr->v[i][k];
}
}
}
/* Build result matrix */
result = matrix_new(n,nx);
for(i=0;i<n;i++) {
for(j=0;j<nx;j++) {
result->v[i][j] = x->v[i][j];
}
}
/* Cleanup */
matrix_delete(qr);
matrix_delete(x);
/* Return the result */
return result;
}
mat matrix_solve_lu(mat xMatrix, mat yMatrix) {
mat lu,x;
double s,t;
int i,n,m,k,j,nx,kmax,p;
int *piv;
int pivsign;
double *LUrowi;
double *LUcolj;
/* Initial validations */
if (xMatrix->m != yMatrix->m) {
printf("Matrix row dimensions must agree.\n");
exit(1);
}
// Use a "left-looking", dot-product, Crout/Doolittle algorithm.
lu = matrix_copy(xMatrix);
m = xMatrix->m;
n = xMatrix->n;
piv = (int*)calloc(m,sizeof(int));
for (i = 0; i < m; i++) {
piv[i] = i;
}
pivsign = 1;
LUcolj = (double*)calloc(m,sizeof(double));
// Outer loop.
for (j = 0; j < n; j++) {
// Make a copy of the j-th column to localize references.
for (i = 0; i < m; i++) {
LUcolj[i] = lu->v[i][j];
}
// Apply previous transformations.
for (i = 0; i < m; i++) {
LUrowi = lu->v[i];
// Most of the time is spent in the following dot product.
kmax = MIN(i, j);
s = 0.0;
for (k = 0; k < kmax; k++) {
s += LUrowi[k] * LUcolj[k];
}
LUrowi[j] = LUcolj[i] -= s;
}
// Find pivot and exchange if necessary.
p = j;
for (i = j + 1; i < m; i++) {
if (fabs(LUcolj[i]) > fabs(LUcolj[p])) {
p = i;
}
}
if (p != j) {
for (k = 0; k < n; k++) {
t = lu->v[p][k];
lu->v[p][k] = lu->v[j][k];
lu->v[j][k] = t;
}
k = piv[p];
piv[p] = piv[j];
piv[j] = k;
pivsign = -pivsign;
}
// Compute multipliers.
if (j < m && lu->v[j][j] != 0.0) {
for (i = j + 1; i < m; i++) {
lu->v[i][j] /= lu->v[j][j];
}
}
}
/* Validate */
for(i=0;i<n;i++) {
if( lu->v[i][i]==0 ) {
printf("Matrix is rank deficient. Data fails to converge.");
exit(1);
}
}
/* Now solve */
// Copy right hand side with pivoting
nx = yMatrix->n;
x = get_matrix(yMatrix,piv,m, 0, nx - 1);
// Solve L*Y = B(piv,:)
for (k = 0; k < n; k++) {
for (i = k + 1; i < n; i++) {
for (j = 0; j < nx; j++) {
x->v[i][j] -= x->v[k][j] * lu->v[i][k];
}
}
}
// Solve U*X = Y;
for (k = n - 1; k >= 0; k--) {
for (j = 0; j < nx; j++) {
x->v[k][j] /= lu->v[k][k];
}
for (i = 0; i < k; i++) {
for (j = 0; j < nx; j++) {
x->v[i][j] -= x->v[k][j] * lu->v[i][k];
}
}
}
/* Cleanup */
free(piv);
free(LUcolj);
matrix_delete(lu);
return x;
}
int main()
{
int i, j, m, n, k, l, p, q, s, frank, kstep, nstep, culaVersion;
double TOL,normM,normU,normS,normV,normP,percent_error,elapsed_secs;
mat *M, *T, *S, *C, *U, *R, *Q, *B;
vec *Icol, *Irow;
time_t start_time, end_time;
char *M_file = "../../matrix_data/A_mat_6kx12k.bin";
//char *M_file = "../data/A_mat_2kx4k.bin";
//char *M_file = "../data/A_mat_1kx2k.bin";
//char *M_file = "../data/A_mat_10x8.bin";
struct timeval start_timeval, end_timeval;
culaStatus status;
printf("Initializing CULA\n");
status = culaInitialize();
checkStatus(status);
culaVersion = culaGetVersion();
printf("culaVersion is %d\n", culaVersion);
printf("loading matrix from %s\n", M_file);
M = matrix_load_from_binary_file(M_file);
m = M->nrows;
n = M->ncols;
printf("sizes of M are %d by %d\n", m, n);
printf("norm(M,fro) = %f\n", get_matrix_frobenius_norm(M));
// now test rank k ID of M..
k = 1000; // rank
p = 20; // oversampling
q = 0; // power scheme power
s = 2; // power scheme orthogonalization amount
kstep = 500; //block step size
nstep = 2;
TOL = 0;
printf("\ncalling rank %d column ID routine..\n", k);
gettimeofday(&start_timeval, NULL);
//id_decomp_fixed_rank_or_prec(M, k, 0, &frank, &Icol, &T);
gettimeofday(&end_timeval, NULL);
elapsed_secs = get_seconds_frac(start_timeval,end_timeval);
printf("elapsed time is: %4.1f\n", elapsed_secs);
printf("check error\n");
//use_id_decomp_for_approximation(M, T, Icol, k);
printf("\ncalling rank %d old QB routine..\n", k);
gettimeofday(&start_timeval, NULL);
//randQB_pb(M, kstep, nstep, q, s, &Q, &B);
gettimeofday(&end_timeval, NULL);
elapsed_secs = get_seconds_frac(start_timeval,end_timeval);
printf("elapsed time is: %4.1f\n", elapsed_secs);
printf("check error\n");
//use_QB_decomp_for_approximation(M, Q, B);
printf("\ncalling rank %d new QB routine..\n", k);
gettimeofday(&start_timeval, NULL);
randQB_pb_new(M, kstep, nstep, TOL, q, s, &frank, &Q, &B);
gettimeofday(&end_timeval, NULL);
elapsed_secs = get_seconds_frac(start_timeval,end_timeval);
printf("elapsed time is: %4.1f\n", elapsed_secs);
printf("check error\n");
use_QB_decomp_for_approximation(M, Q, B);
printf("\ncalling rank %d block randomized column ID routine..\n", k);
p = kstep; // p should be \geq kstep
gettimeofday(&start_timeval, NULL);
id_blockrand_decomp_fixed_rank_or_prec(M, k, p, TOL, kstep, q, s, &frank, &Icol, &T);
gettimeofday(&end_timeval, NULL);
elapsed_secs = get_seconds_frac(start_timeval,end_timeval);
printf("elapsed time is: %4.1f\n", elapsed_secs);
printf("check error\n");
use_id_decomp_for_approximation(M, T, Icol, k);
printf("\ncalling rank %d randomized column ID routine..\n", k);
p = kstep;
gettimeofday(&start_timeval, NULL);
id_rand_decomp_fixed_rank(M, k, p, q, s, &Icol, &T);
gettimeofday(&end_timeval, NULL);
elapsed_secs = get_seconds_frac(start_timeval,end_timeval);
printf("elapsed time is: %4.1f\n", elapsed_secs);
printf("check error\n");
use_id_decomp_for_approximation(M, T, Icol, k);
// delete and exit
//printf("delete and exit..\n");
//matrix_delete(M); matrix_delete(T); matrix_delete(S);
//vector_delete(Icol); vector_delete(Irow);
//printf("Shutting down CULA\n");
//culaShutdown();
//return EXIT_SUCCESS;
printf("\ncalling rank %d two sided ID routine..\n", k);
gettimeofday(&start_timeval, NULL);
//id_two_sided_decomp_fixed_rank_or_prec(M, k, TOL, &frank, &Icol, &Irow, &T, &S);
gettimeofday(&end_timeval, NULL);
elapsed_secs = get_seconds_frac(start_timeval,end_timeval);
printf("elapsed time is: %4.1f\n", elapsed_secs);
printf("check error\n");
//use_id_two_sided_decomp_for_approximation(M, T, S, Icol, Irow, k);
printf("\ncalling rank %d randomized two sided ID routine..\n", k);
p = 20;
gettimeofday(&start_timeval, NULL);
id_two_sided_rand_decomp_fixed_rank(M, k, p, q, s, &Icol, &Irow, &T, &S);
gettimeofday(&end_timeval, NULL);
elapsed_secs = get_seconds_frac(start_timeval,end_timeval);
printf("elapsed time is: %4.1f\n", elapsed_secs);
printf("check error\n");
use_id_two_sided_decomp_for_approximation(M, T, S, Icol, Irow, k);
printf("\ncalling rank %d block randomized two sided ID routine..\n", k);
p = kstep;
gettimeofday(&start_timeval, NULL);
id_two_sided_blockrand_decomp_fixed_rank_or_prec(M, k, p, TOL, kstep, q, s, &frank, &Icol, &Irow, &T, &S);
gettimeofday(&end_timeval, NULL);
elapsed_secs = get_seconds_frac(start_timeval,end_timeval);
printf("elapsed time is: %4.1f\n", elapsed_secs);
printf("check error\n");
use_id_two_sided_decomp_for_approximation(M, T, S, Icol, Irow, k);
printf("\ncalling rank %d CUR routine\n", k);
gettimeofday(&start_timeval, NULL);
//cur_decomp_fixed_rank_or_prec(M, k, TOL, &frank, &C, &U, &R);
gettimeofday(&end_timeval, NULL);
elapsed_secs = get_seconds_frac(start_timeval,end_timeval);
printf("elapsed time is: %4.1f\n", elapsed_secs);
printf("check error\n");
//use_cur_decomp_for_approximation(M, C, U, R);
printf("\ncalling rank %d randomized CUR routine\n", k);
p = 20;
gettimeofday(&start_timeval, NULL);
cur_rand_decomp_fixed_rank(M, k, p, q, s, &C, &U, &R);
gettimeofday(&end_timeval, NULL);
elapsed_secs = get_seconds_frac(start_timeval,end_timeval);
printf("elapsed time is: %4.1f\n", elapsed_secs);
printf("check error\n");
use_cur_decomp_for_approximation(M, C, U, R);
printf("\ncalling rank %d block randomized CUR routine\n", k);
p = kstep;
gettimeofday(&start_timeval, NULL);
cur_blockrand_decomp_fixed_rank_or_prec(M, k, p, TOL, kstep, q, s, &frank, &C, &U, &R);
gettimeofday(&end_timeval, NULL);
elapsed_secs = get_seconds_frac(start_timeval,end_timeval);
printf("elapsed time is: %4.1f\n", elapsed_secs);
printf("check error\n");
use_cur_decomp_for_approximation(M, C, U, R);
// delete and exit
printf("delete and exit..\n");
matrix_delete(M); matrix_delete(T); matrix_delete(S);
vector_delete(Icol); vector_delete(Irow);
printf("Shutting down CULA\n");
culaShutdown();
return EXIT_SUCCESS;
}