QDWHInfo QDWH( Matrix<F>& A, const QDWHCtrl& ctrl )
{
EL_DEBUG_CSE
typedef Base<F> Real;
const Real twoEst = TwoNormEstimate( A );
A *= 1/twoEst;
// The one-norm of the inverse can be replaced with an estimate which is
// a few times cheaper, e.g., via Higham and Tisseur's block algorithm
// from "A Block Algorithm for Matrix 1-Norm Estimation, with an Application
// to 1-Norm Pseudospectra".
Real sMinUpper;
Matrix<F> Y( A );
if( A.Height() > A.Width() )
{
qr::ExplicitTriang( Y );
try
{
TriangularInverse( UPPER, NON_UNIT, Y );
sMinUpper = Real(1) / OneNorm( Y );
} catch( SingularMatrixException& e ) { sMinUpper = 0; }
}
else
{
try
{
Inverse( Y );
sMinUpper = Real(1) / OneNorm( Y );
} catch( SingularMatrixException& e ) { sMinUpper = 0; }
}
return QDWHInner( A, sMinUpper, ctrl );
}
Base<F> Norm( const Matrix<F>& A, NormType type )
{
DEBUG_ONLY(CSE cse("Norm"))
Base<F> norm = 0;
switch( type )
{
// The following norms are rather cheap to compute
case ENTRYWISE_ONE_NORM:
norm = EntrywiseNorm( A, Base<F>(1) );
break;
case FROBENIUS_NORM:
norm = FrobeniusNorm( A );
break;
case INFINITY_NORM:
norm = InfinityNorm( A );
break;
case MAX_NORM:
norm = MaxNorm( A );
break;
case ONE_NORM:
norm = OneNorm( A );
break;
// The following two norms make use of an SVD
case NUCLEAR_NORM:
norm = NuclearNorm( A );
break;
case TWO_NORM:
norm = TwoNorm( A );
break;
}
return norm;
}
T InfinityNorm(const SparseMatrix<T>& A)
{
// the infinity norm is the max absolute row sum; easier to transpose first
// and return the one norm of the transpose
SparseMatrix<T> Atranspose;
Transpose(A, Atranspose);
return OneNorm(Atranspose);
}
Int
Newton( DistMatrix<Field>& A, const SignCtrl<Base<Field>>& ctrl )
{
EL_DEBUG_CSE
typedef Base<Field> Real;
Real tol = ctrl.tol;
if( tol == Real(0) )
tol = A.Height()*limits::Epsilon<Real>();
Int numIts=0;
DistMatrix<Field> B( A.Grid() );
DistMatrix<Field> *X=&A, *XNew=&B;
while( numIts < ctrl.maxIts )
{
// Overwrite XNew with the new iterate
NewtonStep( *X, *XNew, ctrl.scaling );
// Use the difference in the iterates to test for convergence
Axpy( Real(-1), *XNew, *X );
const Real oneDiff = OneNorm( *X );
const Real oneNew = OneNorm( *XNew );
// Ensure that X holds the current iterate and break if possible
++numIts;
std::swap( X, XNew );
if( ctrl.progress && A.Grid().Rank() == 0 )
cout << "after " << numIts << " Newton iter's: "
<< "oneDiff=" << oneDiff << ", oneNew=" << oneNew
<< ", oneDiff/oneNew=" << oneDiff/oneNew << ", tol="
<< tol << endl;
if( oneDiff/oneNew <= Pow(oneNew,ctrl.power)*tol )
break;
}
if( X != &A )
A = *X;
return numIts;
}
ValueInt<Base<F>> InverseFreeSignDivide( ElementalMatrix<F>& XPre )
{
DEBUG_CSE
DistMatrixReadWriteProxy<F,F,MC,MR> XProx( XPre );
auto& X = XProx.Get();
typedef Base<F> Real;
const Grid& g = X.Grid();
const Int n = X.Width();
if( X.Height() != 2*n )
LogicError("Matrix should be 2n x n");
// Expose A and B, and then copy A
auto B = X( IR(0,n ), ALL );
auto A = X( IR(n,2*n), ALL );
DistMatrix<F> ACopy( A );
// Run the inverse-free alternative to Sign
InverseFreeSign( X );
// Compute the pivoted QR decomp of inv(A + B) A [See LAWN91]
// 1) B := A + B
// 2) [Q,R,Pi] := QRP(A)
// 3) B := Q^H B
// 4) [R,Q] := RQ(B)
B += A;
DistMatrix<F,MD,STAR> t(g);
DistMatrix<Base<F>,MD,STAR> d(g);
DistMatrix<Int,VR,STAR> p(g);
QR( A, t, d, p );
qr::ApplyQ( LEFT, ADJOINT, A, t, d, B );
RQ( B, t, d );
// A := Q^H A Q
A = ACopy;
rq::ApplyQ( LEFT, ADJOINT, B, t, d, A );
rq::ApplyQ( RIGHT, NORMAL, B, t, d, A );
// Return || E21 ||1 / || A ||1
// Return || E21 ||1 / || A ||1
ValueInt<Real> part = ComputePartition( A );
part.value /= OneNorm(ACopy);
return part;
}
TwoNormUpperBound( const DistMatrix<F>& A )
{
#ifndef RELEASE
CallStackEntry entry("TwoNormUpperBound");
#endif
typedef BASE(F) R;
const R m = A.Height();
const R n = A.Width();
const R maxNorm = MaxNorm( A );
const R oneNorm = OneNorm( A );
const R infNorm = InfinityNorm( A );
R upperBound = std::min( Sqrt(m*n)*maxNorm, Sqrt(m)*infNorm );
upperBound = std::min( upperBound, Sqrt(n)*oneNorm );
upperBound = std::min( upperBound, Sqrt( oneNorm*infNorm ) );
return upperBound;
}
Base<F> InverseFreeSignDivide( Matrix<F>& X )
{
DEBUG_CSE
typedef Base<F> Real;
const Int n = X.Width();
if( X.Height() != 2*n )
LogicError("Matrix should be 2n x n");
// Expose A and B, and then copy A
auto B = X( IR(0,n ), ALL );
auto A = X( IR(n,2*n), ALL );
Matrix<F> ACopy( A );
// Run the inverse-free alternative to Sign
InverseFreeSign( X );
// Compute the pivoted QR decomp of inv(A + B) A [See LAWN91]
// 1) B := A + B
// 2) [Q,R,Pi] := QRP(A)
// 3) B := Q^H B
// 4) [R,Q] := RQ(B)
B += A;
Matrix<F> t;
Matrix<Base<F>> d;
Matrix<Int> p;
QR( A, t, d, p );
qr::ApplyQ( LEFT, ADJOINT, A, t, d, B );
RQ( B, t, d );
// A := Q^H A Q
A = ACopy;
rq::ApplyQ( LEFT, ADJOINT, B, t, d, A );
rq::ApplyQ( RIGHT, NORMAL, B, t, d, A );
// Return || E21 ||1 / || A ||1
ValueInt<Real> part = ComputePartition( A );
part.value /= OneNorm(ACopy);
return part;
}
inline ValueInt<BASE(F)>
QDWHDivide
( UpperOrLower uplo, DistMatrix<F>& A, DistMatrix<F>& G, bool returnQ=false )
{
DEBUG_ONLY(CallStackEntry cse("herm_eig::QDWHDivide"))
// G := sgn(G)
// G := 1/2 ( G + I )
herm_polar::QDWH( uplo, G );
UpdateDiagonal( G, F(1) );
Scale( F(1)/F(2), G );
// Compute the pivoted QR decomposition of the spectral projection
const Grid& g = A.Grid();
DistMatrix<F,MD,STAR> t(g);
DistMatrix<Int,VR,STAR> p(g);
elem::QR( G, t, p );
// A := Q^H A Q
MakeHermitian( uplo, A );
const Base<F> oneA = OneNorm( A );
if( returnQ )
{
ExpandPackedReflectors( LOWER, VERTICAL, CONJUGATED, 0, G, t );
DistMatrix<F> B(g);
Gemm( ADJOINT, NORMAL, F(1), G, A, B );
Gemm( NORMAL, NORMAL, F(1), B, G, A );
}
else
{
qr::ApplyQ( LEFT, ADJOINT, G, t, A );
qr::ApplyQ( RIGHT, NORMAL, G, t, A );
}
// Return || E21 ||1 / || A ||1 and the chosen rank
auto part = ComputePartition( A );
part.value /= oneA;
return part;
}
int InverseFreeSign( ElementalMatrix<F>& XPre, Int maxIts=100, Base<F> tau=0 )
{
DEBUG_CSE
DistMatrixReadWriteProxy<F,F,MC,MR> XProx( XPre );
auto& X = XProx.Get();
typedef Base<F> Real;
const Grid& g = X.Grid();
const Int n = X.Width();
if( X.Height() != 2*n )
LogicError("X must be 2n x n");
// Compute the tolerance if it is unset
if( tau == Real(0) )
tau = n*limits::Epsilon<Real>();
// Expose A and B in the original and temporary
DistMatrix<F> XAlt( 2*n, n, g );
auto B = X( IR(0,n ), ALL );
auto A = X( IR(n,2*n), ALL );
auto BAlt = XAlt( IR(0,n ), ALL );
auto AAlt = XAlt( IR(n,2*n), ALL );
// Flip the sign of A
A *= -1;
// Set up the space for explicitly computing the left half of Q
DistMatrix<F,MD,STAR> t(g);
DistMatrix<Base<F>,MD,STAR> d(g);
DistMatrix<F> Q( 2*n, n, g );
auto Q12 = Q( IR(0,n ), ALL );
auto Q22 = Q( IR(n,2*n), ALL );
// Run the iterative algorithm
Int numIts=0;
DistMatrix<F> R(g), RLast(g);
while( numIts < maxIts )
{
XAlt = X;
QR( XAlt, t, d );
// Form the left half of Q
Zero( Q12 );
MakeIdentity( Q22 );
qr::ApplyQ( LEFT, NORMAL, XAlt, t, d, Q );
// Save a copy of R
R = BAlt;
MakeTrapezoidal( UPPER, R );
// Form the new iterate
Gemm( ADJOINT, NORMAL, F(1), Q12, A, F(0), AAlt );
Gemm( ADJOINT, NORMAL, F(1), Q22, B, F(0), BAlt );
X = XAlt;
// Use the difference in the iterates to test for convergence
++numIts;
if( numIts > 1 )
{
const Real oneRLast = OneNorm(RLast);
AxpyTrapezoid( UPPER, F(-1), R, RLast );
const Real oneRDiff = OneNorm(RLast);
if( oneRDiff <= tau*oneRLast )
break;
}
RLast = R;
}
// Revert the sign of A and return
A *= -1;
return numIts;
}
This file is part of Elemental and is under the BSD 2-Clause License,
which can be found in the LICENSE file in the root directory, or at
http://opensource.org/licenses/BSD-2-Clause
*/
#include <El.hpp>
namespace El {
template<typename F>
Base<F> OneCondition( const Matrix<F>& A )
{
DEBUG_ONLY(CSE cse("OneCondition"))
typedef Base<F> Real;
Matrix<F> B( A );
const Real oneNorm = OneNorm( B );
try { Inverse( B ); }
catch( SingularMatrixException& e )
{ return limits::Infinity<Real>(); }
const Real oneNormInv = OneNorm( B );
return oneNorm*oneNormInv;
}
template<typename F>
Base<F> OneCondition( const ElementalMatrix<F>& A )
{
DEBUG_ONLY(CSE cse("OneCondition"))
typedef Base<F> Real;
DistMatrix<F> B( A );
const Real oneNorm = OneNorm( B );
try { Inverse( B ); }
part.value /= oneA;
return part;
}
template<typename F>
inline ValueInt<BASE(F)>
RandomizedSignDivide
( UpperOrLower uplo, Matrix<F>& A, Matrix<F>& G,
bool returnQ=false, Int maxIts=1, BASE(F) relTol=0 )
{
DEBUG_ONLY(CallStackEntry cse("herm_eig::RandomizedSignDivide"))
typedef Base<F> Real;
const Int n = A.Height();
MakeHermitian( uplo, A );
const Real oneA = OneNorm( A );
if( relTol == Real(0) )
relTol = 500*n*lapack::MachineEpsilon<Real>();
// S := sgn(G)
// S := 1/2 ( S + I )
auto S( G );
herm_polar::QDWH( uplo, S );
UpdateDiagonal( S, F(1) );
Scale( F(1)/F(2), S );
ValueInt<Real> part;
Matrix<F> V, B, t;
Int it=0;
while( it < maxIts )
{
#ifndef ELEM_SVD_UTIL_HPP
#define ELEM_SVD_UTIL_HPP
#include ELEM_ADJOINT_INC
#include ELEM_ONENORM_INC
namespace elem {
namespace svd {
template<typename F>
inline bool
CheckScale( DistMatrix<F>& A, BASE(F)& scale )
{
scale = 1;
typedef Base<F> Real;
const Real oneNormOfA = OneNorm( A );
const Real safeMin = lapack::MachineSafeMin<Real>();
const Real precision = lapack::MachinePrecision<Real>();
const Real smallNumber = safeMin/precision;
const Real bigNumber = 1/smallNumber;
const Real rhoMin = Sqrt(smallNumber);
const Real rhoMax = Min( Sqrt(bigNumber), 1/Sqrt(Sqrt(safeMin)) );
if( oneNormOfA > 0 && oneNormOfA < rhoMin )
{
scale = rhoMin/oneNormOfA;
return true;
}
else if( oneNormOfA > rhoMax )
{
scale = rhoMax/oneNormOfA;
