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Hey, I was insomniac too ;)

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This restores much of the performance benefit of Euler's method, without compromising accuracy (tested on 1e+7 matrices). Namely, my benchmark now runs in 1.5 s instead of 2.2 s. The same in the default branch runs in 1.08 s instead of 1.9 s, so the default branch benefits even more!
This commit is contained in:
Benoit Jacob 2009-10-28 03:50:29 -04:00
parent 9e15a6da2e
commit 241b9d34a7
1 changed files with 25 additions and 16 deletions
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41
Eigen/src/LU/Inverse.h
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@ -130,22 +130,31 @@ void ei_compute_inverse_in_size4_case(const MatrixType& _matrix, MatrixType* res
typename MatrixType::PlainMatrixType matrix(_matrix); typename MatrixType::PlainMatrixType matrix(_matrix);
// let's extract from the 2 first colums a 2x2 block whose determinant is as big as possible. // let's extract from the 2 first colums a 2x2 block whose determinant is as big as possible.
int good_row0=0, good_row1=1; int good_row0, good_row1, good_i;
RealScalar good_absdet(-1); Matrix<RealScalar,6,1> absdet;
// this double for loop shouldn't be too costly: only 6 iterations
for(int row0=0; row0<4; ++row0) { // any 2x2 block with determinant above this threshold will be considered good enough
for(int row1=row0+1; row1<4; ++row1) RealScalar d = (matrix.col(0).squaredNorm()+matrix.col(1).squaredNorm()) * 1e-2f;
{ #define ei_inv_size4_helper_macro(i,row0,row1) \
RealScalar absdet = ei_abs(matrix.coeff(row0,0)*matrix.coeff(row1,1) absdet[i] = ei_abs(matrix.coeff(row0,0)*matrix.coeff(row1,1) \
- matrix.coeff(row0,1)*matrix.coeff(row1,0)); - matrix.coeff(row0,1)*matrix.coeff(row1,0)); \
if(absdet > good_absdet) if(absdet[i] > d) { good_row0=row0; good_row1=row1; goto good;}
{ ei_inv_size4_helper_macro(0,0,1);
good_absdet = absdet; ei_inv_size4_helper_macro(1,0,2);
good_row0 = row0; ei_inv_size4_helper_macro(2,0,3);
good_row1 = row1; ei_inv_size4_helper_macro(3,1,2);
} ei_inv_size4_helper_macro(4,1,3);
} ei_inv_size4_helper_macro(5,2,3);
}
// no 2x2 block has determinant bigger than the threshold. So just take the one that
// has the biggest determinant
absdet.maxCoeff(&good_i);
good_row0 = good_i <= 2 ? 0 : good_i <= 4 ? 1 : 2;
good_row1 = good_i <= 2 ? good_i+1 : good_i <= 4 ? good_i-1 : 3;
// now good_row0 and good_row1 are correctly set
good:
// do row permutations to move this 2x2 block to the top // do row permutations to move this 2x2 block to the top
matrix.row(0).swap(matrix.row(good_row0)); matrix.row(0).swap(matrix.row(good_row0));
matrix.row(1).swap(matrix.row(good_row1)); matrix.row(1).swap(matrix.row(good_row1));