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Adapted Geometry includes.

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Adapted the decomposition documentation regarding the solve signature.
This commit is contained in:
Hauke Heibel 2010-01-21 09:43:30 +01:00
parent ecc71abdda
commit 7bf5930496
2 changed files with 7 additions and 12 deletions
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1
Eigen/Geometry
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@ -5,7 +5,6 @@
#include "src/Core/util/DisableMSVCWarnings.h" #include "src/Core/util/DisableMSVCWarnings.h"
#include "Array"
#include "SVD" #include "SVD"
#include "LU" #include "LU"
#include <limits> #include <limits>

18
doc/C05_TutorialLinearAlgebra.dox
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@ -148,9 +148,8 @@ The API is the same as when using the %LU decomposition.
MatrixXf D = MatrixXf::Random(8,4); MatrixXf D = MatrixXf::Random(8,4);
MatrixXf A = D.transpose() * D; MatrixXf A = D.transpose() * D;
VectorXf b = A * VectorXf::Random(4); VectorXf b = A * VectorXf::Random(4);
VectorXf x; VectorXf x_llt = A.llt().solve(b); // using a LLT factorization
A.llt().solve(b,&x); // using a LLT factorization VectorXf x_ldlt = A.ldlt().solve(b); // using a LDLT factorization
A.ldlt().solve(b,&x); // using a LDLT factorization
\endcode \endcode
The LLT and LDLT classes also provide an \em in \em place API for the case where the value of the The LLT and LDLT classes also provide an \em in \em place API for the case where the value of the
@ -238,10 +237,9 @@ of its use:
// ... // ...
MatrixXf A = MatrixXf::Random(20,20); MatrixXf A = MatrixXf::Random(20,20);
VectorXf b = VectorXf::Random(20); VectorXf b = VectorXf::Random(20);
VectorXf x; VectorXf x = A.svd().solve(b);
A.svd().solve(b, &x);
SVD<MatrixXf> svdOfA(A); SVD<MatrixXf> svdOfA(A);
svdOfA.solve(b, &x); x = svdOfA.solve(b);
\endcode \endcode
%LU decomposition with full pivoting has better numerical stability than %LU decomposition with %LU decomposition with full pivoting has better numerical stability than %LU decomposition with
@ -252,10 +250,9 @@ partial pivoting. It is defined in the class FullPivLU. The solver can also hand
// ... // ...
MatrixXf A = MatrixXf::Random(20,20); MatrixXf A = MatrixXf::Random(20,20);
VectorXf b = VectorXf::Random(20); VectorXf b = VectorXf::Random(20);
VectorXf x; VectorXf x = A.lu().solve(b);
A.lu().solve(b, &x);
FullPivLU<MatrixXf> luOfA(A); FullPivLU<MatrixXf> luOfA(A);
luOfA.solve(b, &x); x = luOfA.solve(b);
\endcode \endcode
See the section \ref TutorialAdvLU below. See the section \ref TutorialAdvLU below.
@ -273,8 +270,7 @@ You can obtain the LU decomposition of a matrix by calling \link MatrixBase::lu(
#include <Eigen/LU> #include <Eigen/LU>
MatrixXf A = MatrixXf::Random(20,20); MatrixXf A = MatrixXf::Random(20,20);
VectorXf b = VectorXf::Random(20); VectorXf b = VectorXf::Random(20);
VectorXf x; VectorXf x = A.lu().solve(b);
A.lu().solve(b, &x);
\endcode \endcode
Alternatively, you can construct a named LU decomposition, which allows you to reuse it for more than one operation: Alternatively, you can construct a named LU decomposition, which allows you to reuse it for more than one operation: