eigen/Eigen/src/Core/SolveTriangular.h
2008-08-09 20:06:25 +00:00

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_INVERSEPRODUCT_H
#define EIGEN_INVERSEPRODUCT_H
template<typename XprType> struct ei_is_part { enum {value=false}; };
template<typename XprType, unsigned int Mode> struct ei_is_part<Part<XprType,Mode> > { enum {value=true}; };
template<typename Lhs, typename Rhs,
int TriangularPart = ei_is_part<Lhs>::value ? -1 // this is to solve ambiguous specializations
: (int(Lhs::Flags) & LowerTriangularBit)
? Lower
: (int(Lhs::Flags) & UpperTriangularBit)
? Upper
: -1,
int StorageOrder = int(Lhs::Flags) & RowMajorBit ? RowMajor : ColMajor
>
struct ei_trisolve_selector;
// transform a Part xpr to a Flagged xpr
template<typename Lhs, unsigned int LhsMode, typename Rhs, int TriangularPart, int StorageOrder>
struct ei_trisolve_selector<Part<Lhs,LhsMode>,Rhs,TriangularPart,StorageOrder>
{
static void run(const Part<Lhs,LhsMode>& lhs, Rhs& other)
{
ei_trisolve_selector<Flagged<Lhs,LhsMode,0>,Rhs>::run(lhs._expression(), other);
}
};
// forward substitution, row-major
template<typename Lhs, typename Rhs>
struct ei_trisolve_selector<Lhs,Rhs,Lower,RowMajor>
{
typedef typename Rhs::Scalar Scalar;
static void run(const Lhs& lhs, Rhs& other)
{
for(int c=0 ; c<other.cols() ; ++c)
{
if(!(Lhs::Flags & UnitDiagBit))
other.coeffRef(0,c) = other.coeff(0,c)/lhs.coeff(0, 0);
for(int i=1; i<lhs.rows(); ++i)
{
Scalar tmp = other.coeff(i,c) - ((lhs.row(i).start(i)) * other.col(c).start(i)).coeff(0,0);
if (Lhs::Flags & UnitDiagBit)
other.coeffRef(i,c) = tmp;
else
other.coeffRef(i,c) = tmp/lhs.coeff(i,i);
}
}
}
};
// backward substitution, row-major
template<typename Lhs, typename Rhs>
struct ei_trisolve_selector<Lhs,Rhs,Upper,RowMajor>
{
typedef typename Rhs::Scalar Scalar;
static void run(const Lhs& lhs, Rhs& other)
{
const int size = lhs.cols();
for(int c=0 ; c<other.cols() ; ++c)
{
if(!(Lhs::Flags & UnitDiagBit))
other.coeffRef(size-1,c) = other.coeff(size-1, c)/lhs.coeff(size-1, size-1);
for(int i=size-2 ; i>=0 ; --i)
{
Scalar tmp = other.coeff(i,c)
- ((lhs.row(i).end(size-i-1)) * other.col(c).end(size-i-1)).coeff(0,0);
if (Lhs::Flags & UnitDiagBit)
other.coeffRef(i,c) = tmp;
else
other.coeffRef(i,c) = tmp/lhs.coeff(i,i);
}
}
}
};
// forward substitution, col-major
// FIXME the Lower and Upper specialization could be merged using a small helper class
// performing reflexions on the coordinates...
template<typename Lhs, typename Rhs>
struct ei_trisolve_selector<Lhs,Rhs,Lower,ColMajor>
{
typedef typename Rhs::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type Packet;
enum {PacketSize = ei_packet_traits<Scalar>::size};
static void run(const Lhs& lhs, Rhs& other)
{
const int size = lhs.cols();
for(int c=0 ; c<other.cols() ; ++c)
{
/* let's perform the inverse product per block of 4 columns such that we perfectly match
* our optimized matrix * vector product.
*/
int blockyEnd = (std::max(size-5,0)/4)*4;
for(int i=0; i<blockyEnd;)
{
/* Let's process the 4x4 sub-matrix as usual.
* btmp stores the diagonal coefficients used to update the remaining part of the result.
*/
int startBlock = i;
int endBlock = startBlock+4;
Matrix<Scalar,4,1> btmp;
for (;i<endBlock;++i)
{
if(!(Lhs::Flags & UnitDiagBit))
other.coeffRef(i,c) /= lhs.coeff(i,i);
int remainingSize = endBlock-i-1;
if (remainingSize>0)
other.col(c).block(i+1,remainingSize) -= other.coeffRef(i,c) * Block<Lhs,Dynamic,1>(lhs, i+1, i, remainingSize, 1);
btmp.coeffRef(i-startBlock) = -other.coeffRef(i,c);
}
/* Now we can efficiently update the remaining part of the result as a matrix * vector product.
* NOTE in order to reduce both compilation time and binary size, let's directly call
* the fast product implementation. It is equivalent to the following code:
* other.col(c).end(size-endBlock) += (lhs.block(endBlock, startBlock, size-endBlock, endBlock-startBlock)
* * other.col(c).block(startBlock,endBlock-startBlock)).lazy();
*/
// FIXME this is cool but what about conjugate/adjoint expressions ? do we want to evaluate them ?
// this is a more general problem though.
ei_cache_friendly_product_colmajor_times_vector(
size-endBlock, &(lhs.const_cast_derived().coeffRef(endBlock,startBlock)), lhs.stride(),
btmp, &(other.coeffRef(endBlock,c)));
}
/* Now we have to process the remaining part as usual */
int i;
for(i=blockyEnd; i<size-1; ++i)
{
if(!(Lhs::Flags & UnitDiagBit))
other.coeffRef(i,c) /= lhs.coeff(i,i);
/* NOTE we cannot use lhs.col(i).end(size-i-1) because Part::coeffRef gets called by .col() to
* get the address of the start of the row
*/
other.col(c).end(size-i-1) -= other.coeffRef(i,c) * Block<Lhs,Dynamic,1>(lhs, i+1,i, size-i-1,1);
}
if(!(Lhs::Flags & UnitDiagBit))
other.coeffRef(i,c) /= lhs.coeff(i,i);
}
}
};
// backward substitution, col-major
// see the previous specialization for details on the algorithm
template<typename Lhs, typename Rhs>
struct ei_trisolve_selector<Lhs,Rhs,Upper,ColMajor>
{
typedef typename Rhs::Scalar Scalar;
static void run(const Lhs& lhs, Rhs& other)
{
const int size = lhs.cols();
for(int c=0 ; c<other.cols() ; ++c)
{
int blockyEnd = size-1 - (std::max(size-5,0)/4)*4;
for(int i=size-1; i>blockyEnd;)
{
int startBlock = i;
int endBlock = startBlock-4;
Matrix<Scalar,4,1> btmp;
/* Let's process the 4x4 sub-matrix as usual.
* btmp stores the diagonal coefficients used to update the remaining part of the result.
*/
for (; i>endBlock; --i)
{
if(!(Lhs::Flags & UnitDiagBit))
other.coeffRef(i,c) /= lhs.coeff(i,i);
int remainingSize = i-endBlock-1;
if (remainingSize>0)
other.col(c).block(endBlock+1,remainingSize) -= other.coeffRef(i,c) * Block<Lhs,Dynamic,1>(lhs, endBlock+1, i, remainingSize, 1);
btmp.coeffRef(remainingSize) = -other.coeffRef(i,c);
}
ei_cache_friendly_product_colmajor_times_vector(
endBlock+1, &(lhs.const_cast_derived().coeffRef(0,endBlock+1)), lhs.stride(),
btmp, &(other.coeffRef(0,c)));
}
for(int i=blockyEnd; i>0; --i)
{
if(!(Lhs::Flags & UnitDiagBit))
other.coeffRef(i,c) /= lhs.coeff(i,i);
other.col(c).start(i) -= other.coeffRef(i,c) * Block<Lhs,Dynamic,1>(lhs, 0,i, i, 1);
}
if(!(Lhs::Flags & UnitDiagBit))
other.coeffRef(0,c) /= lhs.coeff(0,0);
}
}
};
/** "in-place" version of MatrixBase::solveTriangular() where the result is written in \a other
*
* \sa solveTriangular()
*/
template<typename Derived>
template<typename OtherDerived>
void MatrixBase<Derived>::solveTriangularInPlace(MatrixBase<OtherDerived>& other) const
{
ei_assert(derived().cols() == derived().rows());
ei_assert(derived().cols() == other.rows());
ei_assert(!(Flags & ZeroDiagBit));
ei_assert(Flags & (UpperTriangularBit|LowerTriangularBit));
ei_trisolve_selector<Derived, OtherDerived>::run(derived(), other.derived());
}
/** \returns the product of the inverse of \c *this with \a other, \a *this being triangular.
*
* This function computes the inverse-matrix matrix product inverse(\c *this) * \a other
* It works as a forward (resp. backward) substitution if \c *this is an upper (resp. lower)
* triangular matrix.
*
* It is required that \c *this be marked as either an upper or a lower triangular matrix, as
* can be done by marked(), and as is automatically the case with expressions such as those returned
* by extract().
* Example: \include MatrixBase_marked.cpp
* Output: \verbinclude MatrixBase_marked.out
*
* \sa marked(), extract()
*/
template<typename Derived>
template<typename OtherDerived>
typename OtherDerived::Eval MatrixBase<Derived>::solveTriangular(const MatrixBase<OtherDerived>& other) const
{
typename OtherDerived::Eval res(other);
solveTriangularInPlace(res);
return res;
}
#endif // EIGEN_INVERSEPRODUCT_H