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  /external/chromium/grit/
net_resources.h 3 // Can we solve this better?
  /external/eigen/doc/snippets/
Tutorial_solve_reuse_decomposition.cpp 7 x = luOfA.solve(b);
11 x = luOfA.solve(b);
LLT_solve.cpp 5 // and let's solve samples * [x y]^T = elevations in least square sense:
7 = (samples.adjoint() * samples).llt().solve((samples.adjoint()*elevations));
Tutorial_solve_singular.cpp 8 x = A.lu().solve(b);
Tutorial_solve_triangular.cpp 7 Vector3f x = A.triangularView<Upper>().solve(b);
ColPivHouseholderQR_solve.cpp 6 x = m.colPivHouseholderQr().solve(y);
FullPivHouseholderQR_solve.cpp 6 x = m.fullPivHouseholderQr().solve(y);
HouseholderQR_solve.cpp 7 x = m.householderQr().solve(y);
PartialPivLU_solve.cpp 5 MatrixXd X = A.lu().solve(B);
Tutorial_solve_multiple_rhs.cpp 6 X = A.fullPivLu().solve(B);
FullPivLU_solve.cpp 5 Matrix<float,3,2> x = m.fullPivLu().solve(y);
  /external/eigen/test/
sparse_solvers.cpp 53 VERIFY_IS_APPROX(refMat2.template triangularView<Lower>().solve(vec2),
54 m2.template triangularView<Lower>().solve(vec3));
58 VERIFY_IS_APPROX(refMat2.template triangularView<Upper>().solve(vec2),
59 m2.template triangularView<Upper>().solve(vec3));
60 VERIFY_IS_APPROX(refMat2.conjugate().template triangularView<Upper>().solve(vec2),
61 m2.conjugate().template triangularView<Upper>().solve(vec3));
66 VERIFY_IS_APPROX(refMat2.conjugate().template triangularView<Upper>().solve(vec2),
67 mm2.conjugate().template triangularView<Upper>().solve(vec3));
72 VERIFY_IS_APPROX(refMat2.transpose().template triangularView<Upper>().solve(vec2),
73 m2.transpose().template triangularView<Upper>().solve(vec3))
    [all...]
cholesky.cpp 96 vecX = chollo.solve(vecB);
98 matX = chollo.solve(matB);
104 vecX = cholup.solve(vecB);
106 matX = cholup.solve(matB);
133 vecX = ldltlo.solve(vecB);
135 matX = ldltlo.solve(matB);
140 vecX = ldltup.solve(vecB);
142 matX = ldltup.solve(matB);
154 // check inplace solve
156 VERIFY_EVALUATION_COUNT(matX = ldltlo.solve(matX), 0)
    [all...]
nomalloc.cpp 126 X = LLT.solve(B);
127 x = LLT.solve(b);
129 X = LDLT.solve(B);
130 x = LDLT.solve(b);
142 X = ppLU.solve(B);
143 x = ppLU.solve(b);
145 X = fpLU.solve(B);
146 x = fpLU.solve(b);
150 X = hQR.solve(B);
151 x = hQR.solve(b)
    [all...]
  /prebuilts/tools/common/cglib/
README 1 CGLIB 2.2.3 contains bug fix to solve heavy memory usage issues.
  /external/eigen/doc/examples/
TutorialLinAlgComputeTwice.cpp 17 cout << "The solution is:\n" << llt.solve(b) << endl;
22 cout << "The solution is now:\n" << llt.solve(b) << endl;
TutorialLinAlgExComputeSolveError.cpp 11 MatrixXd x = A.fullPivLu().solve(b);
TutorialLinAlgExSolveColPivHouseholderQR.cpp 15 Vector3f x = A.colPivHouseholderQr().solve(b);
TutorialLinAlgExSolveLDLT.cpp 14 Matrix2f x = A.ldlt().solve(b);
TutorialLinAlgSVDSolve.cpp 14 << A.jacobiSvd(ComputeThinU | ComputeThinV).solve(b) << endl;
Tutorial_PartialLU_solve.cpp 16 Vector3f x = A.lu().solve(b);
  /external/llvm/test/Transforms/Inline/
callgraph-update.ll 3 declare void @solve()
12 call void @solve()
  /external/ceres-solver/docs/
nnlsq.tex 11 is a Non-linear least squares problem~\footnote{Ceres can solve a more general version of this problem, but for pedagogical reasons, we will restrict ourselves to this class of problems for now. See section~\ref{chapter:overview} for a full description of the problems that Ceres can solve}. Here $\|\cdot\|$ denotes the Euclidean norm of a vector.
15 Perhaps the simplest example of such a problem is the problem of Ordinary Linear Regression, where given observations $(x_1,y_1),\hdots, (x_k,y_k)$, we wish to find the line $y = mx + c$, that best explains $y$ as a function of $x$. One way to solve this problem is to find the solution to the following optimization problem
23 This is a non-linear regression problem and solving it by hand is much more tedious. Ceres is designed to help you model and solve problems like this easily and efficiently
  /external/eigen/Eigen/
Cholesky 22 #include "src/misc/Solve.h"
SparseCholesky 23 #include "src/misc/Solve.h"

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