IntegratorExport* createDiagonallyIRK4Export( UserInteraction* _userInteraction,
const std::string &_commonHeaderName)
{
const double alpha = 1.137158042603258;
DMatrix AA(3,3);
DVector bb(3);
DVector cc(3);
AA(0,0) = (1.0+alpha)/2.0; AA(0,1) = 0.0; AA(0,2) = 0.0;
AA(1,0) = -alpha/2.0; AA(1,1) = (1.0+alpha)/2.0; AA(1,2) = 0.0;
AA(2,0) = 1.0+alpha; AA(2,1) = -(1.0+2.0*alpha); AA(2,2) = (1.0+alpha)/2.0;
bb(0) = 1.0/(6.0*alpha*alpha);
bb(1) = 1.0-1.0/(3.0*alpha*alpha);
bb(2) = 1.0/(6.0*alpha*alpha);
cc(0) = (1.0+alpha)/2.0;
cc(1) = 1.0/2.0;
cc(2) = (1.0-alpha)/2.0;
DiagonallyImplicitRKExport* integrator = createDiagonallyImplicitRKExport(_userInteraction, _commonHeaderName);
integrator->initializeButcherTableau(AA, bb, cc);
return integrator;
}
void CCBuffer::skipData(unsigned int u_len)
{
BEGIN_IF(LXE(CA(_u_read_pos, u_len), _u_content_size))
AA(_u_read_pos, u_len);
BEGIN_ELSE
AA(_u_read_pos, (CS(_u_content_size, _u_read_pos)));
END_IF
}
void CCBuffer::readData(char* p_out_data, unsigned int u_len)
{
BEGIN_IF(isReadable(u_len))
memcpy(p_out_data, CA(_p_buffer, _u_read_pos), u_len);
AA(_u_read_pos, u_len);
BEGIN_ELSE_IF(LQ(LDE(CS(_u_content_size, _u_read_pos),0), LNE(u_len, 0)))
DO_ASSIGN(u_len, CS(_u_content_size, _u_read_pos));
memcpy(p_out_data, CA(_p_buffer, _u_read_pos), u_len);
AA(_u_read_pos, u_len);
END_IF
}
void CCBuffer::moveRight(unsigned int u_len)
{
BEGIN_IF(LE(_u_content_size, 0))
DO_RETURN;
END_IF
_reallocBufferSizeInChanged(u_len);
BEGIN_FOR(DO_ASSIGN(int i, CS(_u_content_size, 1)), LDE(i, 0), SSI(i))
DO_ASSIGN(QV(CA(CA(_p_buffer, i), TO_INT(u_len))), QV(CA(_p_buffer, i)));
DO_ASSIGN(QV(CA(_p_buffer, i)), 0);
END_FOR
AA(_u_write_pos, u_len);
AA(_u_read_pos, u_len);
AA(_u_mark_pos, u_len);
AA(_u_content_size, u_len);
}
KOKKOS_INLINE_FUNCTION
int
Gemm<Trans::ConjTranspose,Trans::NoTranspose,
AlgoGemm::SparseSparseSuperNodes,Variant::One>
::invoke(PolicyType &policy,
MemberType &member,
const ScalarType alpha,
CrsExecViewTypeA &A,
CrsExecViewTypeB &B,
const ScalarType beta,
CrsExecViewTypeC &C) {
if (member.team_rank() == 0) {
DenseMatrixView<typename CrsExecViewTypeA::flat_mat_base_type> AA(A.Flat());
DenseMatrixView<typename CrsExecViewTypeA::flat_mat_base_type> BB(B.Flat());
DenseMatrixView<typename CrsExecViewTypeA::flat_mat_base_type> CC(C.Flat());
Gemm<Trans::ConjTranspose,Trans::NoTranspose,
AlgoGemm::ExternalBlas,Variant::One>
::invoke(policy, member,
alpha, AA, BB, beta, CC);
}
return 0;
}
int main(int argc, char* argv[])
{
char tipo1, tipo2;
int numeroV, numeroA, vertice1, vertice2, peso = 1, i = 0, j = 0;
tipo1 = 'D';
tipo2 = 'M';
scanf("%d %d", &numeroV, &numeroA);
createGraph(G, tipo1, tipo2, numeroV, numeroA);
//IG(G);
for(i = 0; i < numeroA; i++)
{
scanf("%d %d", &vertice1, &vertice2);
AA(G, vertice1, vertice2, peso);
}
int origem, destino;
/*for(i = 0; i < numeroA; i++)
{
scanf("%d %d", &origem, &destino);
}*/
while(scanf("%d %d", &origem, &destino) != EOF)
{
path(G, origem, destino);
printf("%d %d\n", origem, destino);
}
return 0;
}
returnValue ExplicitRungeKutta2Export::initializeButcherTableau() {
AA = Matrix(2,2);
bb = Vector(2);
cc = Vector(2);
AA(0,0) = 0.0; AA(0,1) = 0.0;
AA(1,0) = 1.0/2.0; AA(1,1) = 0.0;
bb(0) = 0.0;
bb(1) = 1.0;
cc(0) = 0.0;
cc(1) = 1.0/2.0;
return SUCCESSFUL_RETURN;
}
KOKKOS_INLINE_FUNCTION
int
Herk<Uplo::Upper,Trans::ConjTranspose,
AlgoHerk::SparseSparseSuperNodesByBlocks,Variant::One>
::invoke(PolicyType &policy,
MemberType &member,
const ScalarType alpha,
CrsExecViewTypeA &A,
const ScalarType beta,
CrsExecViewTypeC &C) {
if (member.team_rank() == 0) {
DenseMatrixView<typename CrsExecViewTypeA::hier_mat_base_type> AA(A.Hier());
DenseMatrixView<typename CrsExecViewTypeA::hier_mat_base_type> CC(C.Hier());
Herk<Uplo::Upper,Trans::ConjTranspose,
AlgoHerk::DenseByBlocks,Variant::One>
::invoke(policy, member,
alpha, AA, beta, CC);
}
return 0;
}
/* SUBGOAL connect-to ?grasper ?object1 ?object2 */
void PA_ConnectTo(Context *cx, Subgoal *sg, Ts *ts, Obj *a, Obj *o)
{
Dur d;
Dbg(DBGPLAN, DBGOK, "PA_Connect", E);
switch (sg->state) {
case STBEGIN:
if (!DbIsPartOf(ts, NULL, I(o,1), a)) {
Dbg(DBGPLAN, DBGDETAIL, "failure: grasper not part of actor", E);
goto failure;
}
SG(cx, sg, 1, STFAILURE, L(N("grasp"), I(o,1), I(o,2), E));
return;
case 1:
SG(cx, sg, 2, STFAILURE, L(N("near-graspable"), I(o,1), I(o,3), E));
return;
case 2:
d = DurationOf(I(o, 0));
AA(ts, d, o);
TsIncrement(ts, d);
TOSTATE(cx, sg, 3);
return;
case 3: /* todo: Perhaps merge release into this type of connect? */
SG(cx, sg, 4, STFAILURE, L(N("release"), I(o,1), I(o,2), E));
return;
case 4:
AS(ts, 0, L(N("connected-to"), I(o,2), I(o,3), E));
TOSTATE(cx, sg, STSUCCESS);
return;
default: Dbg(DBGPLAN, DBGBAD, "PA_Connect: undefined state %d", sg->state);
}
failure:
TOSTATE(cx, sg, STFAILURE);
}
/* Initializes variables to specified size */
void gen_test_problem(real** A, real ** B,
real ** C, int const m, int const n)
{
real * a;
real * b;
real * c;
a = (real*) malloc(n*m*sizeof(real));
b = (real*) malloc(m*sizeof(real));
c = (real*) malloc(n*sizeof(real));
for(int idx = 0; idx < n; ++idx)
{
for(int idy = 0; idy < m; ++idy)
{
AA(idy,idx)=1;
}
}
for(int idx = 0; idx < m; ++idx)
{
b[idx]=1;
}
for(int idx = 0; idx < n; ++idx)
{
c[idx]=1;
}
*A = a;
*B = b;
*C = c;
}
/* SUBGOAL pour-onto ?grasper ?holding ?onto */
void PA_PourOnto(Context *cx, Subgoal *sg, Ts *ts, Obj *a, Obj *o)
{
Dur d;
Dbg(DBGPLAN, DBGOK, "PA_PourOnto", E);
switch (sg->state) {
case STBEGIN:
if (!DbIsPartOf(ts, NULL, I(o,1), a)) {
Dbg(DBGPLAN, DBGDETAIL, "failure: grasper not part of actor", E);
goto failure;
}
SG(cx, sg, 2, STFAILURE, L(N("holding"), I(o,1), I(o,2), E));
return;
case 2:
SG(cx, sg, 3, STFAILURE, L(N("near-graspable"), I(o,1), I(o,3), E));
return;
case 3:
d = DurationOf(I(o, 0));
AA(ts, d, o);
TsIncrement(ts, d);
TOSTATE(cx, sg, STSUCCESS); return;
default: Dbg(DBGPLAN, DBGBAD, "PA_PourOnto: undefined state %d", sg->state);
}
failure:
TOSTATE(cx, sg, STFAILURE);
}
/* SUBGOAL action-close ?grasper ?object */
void PA_ActionClose(Context *cx, Subgoal *sg, Ts *ts, Obj *a, Obj *o)
{
Dur d;
Dbg(DBGPLAN, DBGOK, "PA_ActionClose", E);
switch (sg->state) {
case STBEGIN:
if (!DbIsPartOf(ts, NULL, I(o,1), a)) {
Dbg(DBGPLAN, DBGDETAIL, "failure: grasper not part of actor", E);
goto failure;
}
/* todo: Cannot already be holding too much. */
SG(cx, sg, 1, STFAILURE, L(N("near-graspable"), I(o,1), I(o,2), E));
return;
case 1:
d = DurationOf(I(o, 0)); /* Depends on object. */
AA(ts, d, o);
TsIncrement(ts, d);
if (!TE(ts, L(N("open"), I(o,2), E))) {
Dbg(DBGPLAN, DBGBAD, "object not already open", E);
}
AS(ts, 0, L(N("closed"), I(o,2), E));
TOSTATE(cx, sg, STSUCCESS); return;
default:
Dbg(DBGPLAN, DBGBAD, "PA_ActionClose: undefined state %d", sg->state);
}
failure:
TOSTATE(cx, sg, STFAILURE);
}
returnValue DiagonallyIRK3Export::initializeButcherTableau() {
AA = Matrix(numStages,numStages);
bb = Vector(numStages);
cc = Vector(numStages);
AA(0,0) = 1.0/2.0+1.0/(2.0*sqrt(3.0)); AA(0,1) = 0.0;
AA(1,0) = -1.0/sqrt(3.0); AA(1,1) = 1.0/2.0+1.0/(2.0*sqrt(3.0));
bb(0) = 1.0/2.0;
bb(1) = 1.0/2.0;
cc(0) = 1.0/2.0+1.0/(2.0*sqrt(3.0));
cc(1) = 1.0/2.0-1.0/(2.0*sqrt(3.0));
return SUCCESSFUL_RETURN;
}
void CCBuffer::writeData(const char* p_data, unsigned int u_len)
{
DO_ASSERT(LQ(p_data, LD(u_len,0)), "LQ(p_data, LD(u_len,0))");
_reallocBufferSizeInChanged(u_len);
memcpy(CA(_p_buffer, _u_write_pos), p_data, u_len);
AA(_u_write_pos, u_len);
MAXA(_u_content_size, _u_write_pos);
}
returnValue RadauIIA3Export::initializeButcherTableau() {
AA = Matrix(2,2);
bb = Vector(2);
cc = Vector(2);
AA(0,0) = 5/(double)12;
AA(0,1) = -1/(double)12;
AA(1,0) = 3/(double)4;
AA(1,1) = 1/(double)4;
bb(0) = 3/(double)4;
bb(1) = 1/(double)4;
cc(0) = 1/(double)3;
cc(1) = 1;
return SUCCESSFUL_RETURN;
}
IntegratorExport* createGaussLegendre2Export( UserInteraction* _userInteraction,
const std::string &_commonHeaderName)
{
DMatrix AA(1,1);
DVector bb(1);
DVector cc(1);
AA(0,0) = 1.0/2.0;
bb(0) = 1.0;
cc(0) = 1.0/2.0;
ImplicitRungeKuttaExport* integrator = createImplicitRungeKuttaExport(_userInteraction, _commonHeaderName);
integrator->initializeButcherTableau(AA, bb, cc);
return integrator;
}
static PetscErrorCode
RHSJacobian_function( TS ts, double t_, Vec u, Mat A, Mat B, void* G_u )
{
Vector U( u, Vector::owner::other );
Matrix AA( A, false );
Matrix BB( B, false );
TimeStepper T( ts, false );
( *(Jac*)G_u )( U, AA, BB, T, t_ );
return 0;
}
int simEmbGetRotationAxis(const float* quaternionStart,const float* quaternionGoal,float* axis,float* angle)
{
if (!hasLaunched())
return(-1);
// V-REP quaternion, internally: w x y z
// V-REP quaternion, at interfaces: x y z w (like ROS)
C4Vector qStart;
qStart(0)=quaternionStart[3];
qStart(1)=quaternionStart[0];
qStart(2)=quaternionStart[1];
qStart(3)=quaternionStart[2];
C4Vector qGoal;
qGoal(0)=quaternionGoal[3];
qGoal(1)=quaternionGoal[0];
qGoal(2)=quaternionGoal[1];
qGoal(3)=quaternionGoal[2];
// Following few lines taken from the quaternion interpolation part:
C4Vector AA(qStart);
C4Vector BB(qGoal);
if (AA(0)*BB(0)+AA(1)*BB(1)+AA(2)*BB(2)+AA(3)*BB(3)<0.0f)
AA=AA*-1.0f;
C4Vector r((AA.getInverse()*BB).getAngleAndAxis());
C3Vector v(r(1),r(2),r(3));
v=AA*v;
axis[0]=v(0);
axis[1]=v(1);
axis[2]=v(2);
float l=sqrt(v(0)*v(0)+v(1)*v(1)+v(2)*v(2));
if (l!=0.0f)
{
axis[0]/=l;
axis[1]/=l;
axis[2]/=l;
}
angle[0]=r(0);
return(1);
}
/**
* AA1:
* "&&" B AA2
* AA2.st = AA1.st || B.val
* AA1.val = AA2.val
* AA:
* empty
* AA.val = AA.st
*/
int AA(int st){
if (lex(0) == '&'){
lex(1);
if (lex(0) == '&'){
lex(1);
return AA(st && B());
}
} else {
return st;
}
}
IntegratorExport* createGaussLegendre4Export( UserInteraction* _userInteraction,
const String &_commonHeaderName)
{
Matrix AA(2,2);
Vector bb(2);
Vector cc(2);
AA(0,0) = 1.0/4.0; AA(0,1) = (1.0/4.0+sqrt(3.0)/6.0);
AA(1,0) = (1.0/4.0-sqrt(3.0)/6.0); AA(1,1) = 1.0/4.0;
bb(0) = 1.0/2.0;
bb(1) = 1.0/2.0;
cc(0) = 1.0/2.0+sqrt(3.0)/6.0;
cc(1) = 1.0/2.0-sqrt(3.0)/6.0;
ImplicitRungeKuttaExport* integrator = createImplicitRungeKuttaExport(_userInteraction, _commonHeaderName);
integrator->initializeButcherTableau(AA, bb, cc);
return integrator;
}
IntegratorExport* createDiagonallyIRK3Export( UserInteraction* _userInteraction,
const String &_commonHeaderName)
{
Matrix AA(2,2);
Vector bb(2);
Vector cc(2);
AA(0,0) = 1.0/2.0+1.0/(2.0*sqrt(3.0)); AA(0,1) = 0.0;
AA(1,0) = -1.0/sqrt(3.0); AA(1,1) = 1.0/2.0+1.0/(2.0*sqrt(3.0));
bb(0) = 1.0/2.0;
bb(1) = 1.0/2.0;
cc(0) = 1.0/2.0+1.0/(2.0*sqrt(3.0));
cc(1) = 1.0/2.0-1.0/(2.0*sqrt(3.0));
DiagonallyImplicitRKExport* integrator = createDiagonallyImplicitRKExport(_userInteraction, _commonHeaderName);
integrator->initializeButcherTableau(AA, bb, cc);
return integrator;
}
/* Convert back to max c^{T}x s.t. Ax \leq{} b */
void from_standard_form(real * const a, real const *const b, real const * const c,
int const m, int const n)
{
/* Scale up by b */
for (int jj = 0; jj < n; ++jj)
{
for(int ii = 0; ii < m; ++ii)
{
AA(ii,jj) = AA(ii,jj) * b[ii];
}
}
/* Scale up by c */
for (int jj = 0; jj < n; ++jj)
{
for(int ii = 0; ii < m; ++ii)
{
AA(ii,jj) = AA(ii,jj) * c[jj];
}
}
}
int main(int, char**)
{
typedef mtl::dense2D<double> Matrix;
typedef mtl::dense_vector<double> Vector;
Matrix A(4, 4), L(4, 4), U(4, 4), AA(4, 4);
Vector v(4);
double c=1.0;
for (unsigned i= 0; i < 4; i++)
for(unsigned j= 0; j < 4; j++) {
U[i][j]= i <= j ? c * (i+j+2) : (0);
L[i][j]= i > j ? c * (i+j+1) : (i == j ? (1) : (0));
}
std::cout << "L is:\n" << L << "U is:\n" << U;
A= L * U;
std::cout << "A is:\n" << A;
AA= adjoint(A);
for (unsigned i= 0; i < 4; i++)
v[i]= double(i);
Vector b( A*v ), b2( adjoint(A)*v );
Matrix LU(A);
lu(LU);
std::cout << "LU decomposition of A is:\n" << LU;
Matrix B( lu_f(A) );
std::cout << "LU decomposition of A (as function result) is:\n" << B;
Vector v1( lu_solve_straight(A, b) );
std::cout << "v1 is " << v1 << "\n";
Vector v2( lu_solve(A, b) );
std::cout << "v2 is " << v2 << "\n";
mtl::dense_vector<unsigned> P;
lu(A, P);
std::cout << "LU with pivoting is \n" << with_format(A, 5, 2) << "Permutation is " << P << "\n";
Vector v3( lu_apply(A, P, b) );
std::cout << "v3 is " << v3 << "\n";
Vector v4(lu_adjoint_apply(A, P, b2));
std::cout << "v4 is " << v4 << "\n";
Vector v5(lu_adjoint_solve(AA, b));
std::cout << "v5 is " << v5 << "\n";
return 0;
}
/**
\ingroup matop
Matrix exponential.
The matrix exponential is calculated using the Pade approximation adapted from Moler, Cleve; Van Loan, Charles F. (2003), "Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later"
The main use of the matrix exponential is to solve linear ordinary differential equation (ODE) systems:
\f[
\frac{d}{dt}y(t) = Ay(t)\ , \ \mbox{with } y(0) = y_0
\f]
\item then the solution becomes
\f[
y(t) = e^{At}y_0
\f]
\param A square df1b2matrix
\returns The matrix exponential of A
*/
df1b2matrix expm(const df1b2matrix & A)
{
RETURN_ARRAYS_INCREMENT();
int rmin = A.rowmin();
int rmax = A.rowmax();
if(rmax != A.colmax())
{cout<<"Error: Not square matrix in expm."<<endl; ad_exit(1);}
if(rmin != A.colmin())
{cout<<"Error: Not square matrix in expm."<<endl; ad_exit(1);}
df1b2matrix I(rmin,rmax,rmin,rmax);
df1b2matrix AA(rmin,rmax,rmin,rmax);
df1b2matrix X(rmin,rmax,rmin,rmax);
df1b2matrix E(rmin,rmax,rmin,rmax);
df1b2matrix D(rmin,rmax,rmin,rmax);
df1b2matrix cX(rmin,rmax,rmin,rmax);
I.initialize();
for(int i = rmin; i<=rmax; ++i){I(i,i) = 1.0;}
df1b2variable log2NormInf;
log2NormInf = log(max(rowsum(fabs(value(A)))));
log2NormInf/=log(2.0);
int e = (int)value(log2NormInf) + 1;
int s = e+1;
s = (s<0) ? 0 : s;
AA = 1.0/pow(2.0,s)*A;
X = AA;
df1b2variable c = 0.5;
E = I+c*AA;
D = I-c*AA;
int q = 6, p = 1;
for(int k = 2; k<=q; ++k){
c*=((double)q-k+1.0)/((double)k*(2*q-k+1));
X = AA*X;
cX = c*X;
E+=cX;
if(p==1){D+=cX;}else{D-=cX;}
p = (p==1) ? 0 : 1;
}
// E = inv(D)*E;
E = solve(D,E);
for(int k = 1; k<=s; ++k){
E = E*E;
}
RETURN_ARRAYS_DECREMENT();
return E;
}
int
Quad4FiberOverlay::getEltBb(double Xi, double Eta)
{
Matrix B(SL_NUM_NDF,SL_NUM_NODE);
B.Zero();
Bb.Zero();
this->UpdateBase(Xi,Eta);
this->Dual();
for(int i=0; i<4; i++) {
for(int j =0; j<2;j++){
B(j,i) = dNidxAlphai(i,0)*dualg1(j) + dNidxAlphai(i,1)*dualg2(j);
}
//B(0,i) = dNidxAlphai(i,0)*dualg1(0) + dNidxAlphai(i,1)*dualg1(1);
//B(1,i) = dNidxAlphai(i,0)*dualg2(0) + dNidxAlphai(i,1)*dualg2(1);
//opserr << "B = " << B(0,i) << B(1,i) << endln;
}
for(int i =0; i<4;i++) {
Bb[(i+1)*2-2] = AA(0)*B(0,i)+AA(2)*B(1,i);
Bb[(i+1)*2-1] = AA(1)*B(1,i)+AA(2)*B(0,i);
}
return 0;
}
inline
Stat
Herk<Uplo::Upper,Trans::ConjTranspose,
AlgoHerk::SparseSparseSuperNodesByBlocks,Variant::One>
::stat(const ScalarType alpha,
CrsExecViewTypeA &A,
const ScalarType beta,
CrsExecViewTypeC &C) {
DenseMatrixView<typename CrsExecViewTypeA::hier_mat_base_type> AA(A.Hier());
DenseMatrixView<typename CrsExecViewTypeA::hier_mat_base_type> CC(C.Hier());
return Herk<Uplo::Upper,Trans::ConjTranspose,
AlgoHerk::DenseByBlocks,Variant::One>
::stat(alpha, AA, beta, CC);
}
IntegratorExport* createExplicitRungeKutta3Export( UserInteraction* _userInteraction,
const std::string &_commonHeaderName)
{
DMatrix AA(3,3);
DVector bb(3);
DVector cc(3);
AA(0,0) = 0.0; AA(0,1) = 0.0; AA(0,2) = 0.0;
AA(1,0) = 1.0/3.0; AA(1,1) = 0.0; AA(1,2) = 0.0;
AA(2,0) = 0.0; AA(2,1) = 2.0/3.0; AA(2,2) = 0.0;
bb(0) = 1.0/4.0;
bb(1) = 0.0;
bb(2) = 3.0/4.0;
cc(0) = 0.0;
cc(1) = 1.0/3.0;
cc(2) = 2.0/3.0;
ExplicitRungeKuttaExport* integrator = createExplicitRungeKuttaExport(_userInteraction, _commonHeaderName);
integrator->initializeButcherTableau(AA, bb, cc);
return integrator;
}
//-----------------------------------------------------------------------------
bool matrix::lsq_solve(vector<double>& x, vector<double>& b)
{
if ((int) x.size() != m_nc) return false;
if ((int) b.size() != m_nr) return false;
vector<double> y(m_nc);
mult_transpose(b, y);
matrix AA(m_nc, m_nc);
mult_transpose_self(AA);
AA.solve(x, y);
return true;
}
string getHint(string secret, string guess) {
int A = 0 , B = 0 , n = (int)secret.size ();
vector <int> AA (10 , 0) , BB (10 , 0);
for (int i = 0 ; i < n ; ++ i) {
if (secret[i] == guess[i]) A ++;
else {
AA[secret[i] - '0'] ++;
BB[guess[i] - '0'] ++;
}
}
for (int i = 0 ; i < 10 ; i ++) {
B += min (AA[i] , BB[i]);
}
char str[100];
sprintf (str , "%dA%dB" , A , B);
return string (str);
}
inline
Stat
Gemm<Trans::ConjTranspose,Trans::NoTranspose,
AlgoGemm::SparseSparseSuperNodes,Variant::One>
::stat(const ScalarType alpha,
CrsExecViewTypeA &A,
CrsExecViewTypeB &B,
const ScalarType beta,
CrsExecViewTypeC &C) {
DenseMatrixView<typename CrsExecViewTypeA::flat_mat_base_type> AA(A.Flat());
DenseMatrixView<typename CrsExecViewTypeA::flat_mat_base_type> BB(B.Flat());
DenseMatrixView<typename CrsExecViewTypeA::flat_mat_base_type> CC(C.Flat());
return Gemm<Trans::ConjTranspose,Trans::NoTranspose,
AlgoGemm::ExternalBlas,Variant::One>
::stat(alpha, AA, BB, beta, CC);
}
