Searched refs:blueNorm (Results 1 - 15 of 15) sorted by relevance
| /external/eigen/bench/ |
| H A D | bench_norm.cpp | 21 EIGEN_DONT_INLINE typename T::Scalar blueNorm(const T& v) function
23 return v.blueNorm();
91 return v.blueNorm();
245 std::cout << "blueNorm\t" << blueNorm(vf) << "\t" << blueNorm(vd) << "\n";
265 std::cout << "blueNorm\t" << blueNorm(vf) << "\t" << blueNorm(vd) << "\t" << blueNorm(v
[all...] |
| /external/eigen/unsupported/Eigen/src/LevenbergMarquardt/ |
| H A D | LMpar.h | 74 dxnorm = wa2.blueNorm();
88 temp = wa1.blueNorm();
121 dxnorm = wa2.blueNorm();
140 temp = wa1.blueNorm();
|
| H A D | LMonestep.h | 42 m_wa2(j) = m_fjac.col(j).blueNorm();
|
| /external/eigen/unsupported/Eigen/src/NonLinearOptimization/ |
| H A D | lmpar.h | 63 dxnorm = wa2.blueNorm();
87 temp = wa1.blueNorm();
120 dxnorm = wa2.blueNorm();
141 temp = wa1.blueNorm();
211 dxnorm = wa2.blueNorm();
225 temp = wa1.blueNorm();
259 dxnorm = wa2.blueNorm();
279 temp = wa1.blueNorm();
|
| H A D | HybridNonLinearSolver.h | 202 wa2 = fjac.colwise().blueNorm();
445 wa2 = fjac.colwise().blueNorm();
|
| H A D | LevenbergMarquardt.h | 224 wa2 = fjac.colwise().blueNorm();
461 wa2 = fjac.colwise().blueNorm();
|
| /external/eigen/test/ |
| H A D | stable_norm.cpp | 79 VERIFY_IS_APPROX(vrand.blueNorm(), vrand.norm());
92 VERIFY_IS_APPROX(vbig.blueNorm(), sqrt(size)*abs(big));
99 VERIFY_IS_APPROX(vsmall.blueNorm(), sqrt(size)*abs(small));
104 VERIFY_IS_APPROX(vrand.colwise().blueNorm(), vrand.colwise().norm());
107 VERIFY_IS_APPROX(vrand.rowwise().blueNorm(), vrand.rowwise().norm());
|
| H A D | sparse_vector.cpp | 81 VERIFY_IS_APPROX(v1.blueNorm(), refV1.blueNorm());
|
| /external/eigen/Eigen/src/SparseCore/ |
| H A D | SparseDot.h | 95 SparseMatrixBase<Derived>::blueNorm() const function in class:Eigen::SparseMatrixBase
|
| H A D | SparseMatrixBase.h | 393 RealScalar blueNorm() const;
|
| /external/eigen/Eigen/src/Core/ |
| H A D | StableNorm.h | 147 * is faster than blueNorm(). Otherwise the blueNorm() is much faster.
149 * \sa norm(), blueNorm(), hypotNorm()
184 MatrixBase<Derived>::blueNorm() const function in class:Eigen::MatrixBase
|
| H A D | VectorwiseOp.h | 122 EIGEN_MEMBER_FUNCTOR(blueNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
328 * \sa DenseBase::blueNorm() */
329 const typename ReturnType<internal::member_blueNorm,RealScalar>::Type blueNorm() const function in class:Eigen::VectorwiseOp
|
| H A D | MatrixBase.h | 208 RealScalar blueNorm() const;
|
| /external/eigen/unsupported/test/ |
| H A D | NonLinearOptimization.cpp | 186 VERIFY_IS_APPROX(lm.fvec.blueNorm(), 0.09063596);
215 fnorm = lm.fvec.blueNorm();
300 VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08);
335 VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08);
388 VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08);
419 VERIFY_IS_APPROX(solver.fvec.blueNorm(), 1.192636e-08);
491 VERIFY_IS_APPROX(lm.fvec.blueNorm(), 0.09063596);
520 fnorm = lm.fvec.blueNorm();
576 VERIFY_IS_APPROX(fvec.blueNorm(), 0.09063596);
606 fnorm = lm.fvec.blueNorm();
[all...] |
| H A D | levenberg_marquardt.cpp | 79 VERIFY_IS_APPROX(lm.fvec().blueNorm(), 0.09063596);
108 fnorm = lm.fvec().blueNorm();
181 VERIFY_IS_APPROX(fvec.blueNorm(), 0.09063596);
211 fnorm = lm.fvec().blueNorm();
|
Completed in 145 milliseconds