/external/chromium/grit/ |
net_resources.h | 3 // Can we solve this better?
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/external/eigen/doc/snippets/ |
Tutorial_solve_reuse_decomposition.cpp | 7 x = luOfA.solve(b); 11 x = luOfA.solve(b);
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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));
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Tutorial_solve_singular.cpp | 8 x = A.lu().solve(b);
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Tutorial_solve_triangular.cpp | 7 Vector3f x = A.triangularView<Upper>().solve(b);
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ColPivHouseholderQR_solve.cpp | 6 x = m.colPivHouseholderQr().solve(y);
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FullPivHouseholderQR_solve.cpp | 6 x = m.fullPivHouseholderQr().solve(y);
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HouseholderQR_solve.cpp | 7 x = m.householderQr().solve(y);
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PartialPivLU_solve.cpp | 5 MatrixXd X = A.lu().solve(B);
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Tutorial_solve_multiple_rhs.cpp | 6 X = A.fullPivLu().solve(B);
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FullPivLU_solve.cpp | 5 Matrix<float,3,2> x = m.fullPivLu().solve(y);
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/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.
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/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;
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TutorialLinAlgExComputeSolveError.cpp | 11 MatrixXd x = A.fullPivLu().solve(b);
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TutorialLinAlgExSolveColPivHouseholderQR.cpp | 15 Vector3f x = A.colPivHouseholderQr().solve(b);
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TutorialLinAlgExSolveLDLT.cpp | 14 Matrix2f x = A.ldlt().solve(b);
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TutorialLinAlgSVDSolve.cpp | 14 << A.jacobiSvd(ComputeThinU | ComputeThinV).solve(b) << endl;
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Tutorial_PartialLU_solve.cpp | 16 Vector3f x = A.lu().solve(b);
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/external/llvm/test/Transforms/Inline/ |
callgraph-update.ll | 3 declare void @solve() 12 call void @solve()
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/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
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/external/eigen/Eigen/ |
Cholesky | 22 #include "src/misc/Solve.h"
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SparseCholesky | 23 #include "src/misc/Solve.h"
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