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  /external/eigen/test/
qr.cpp 50 m2 = qr.solve(m3);
73 m2 = qr.solve(m3);
93 VERIFY_RAISES_ASSERT(qr.solve(tmp))
qr_fullpivoting.cpp 48 m2 = qr.solve(m3);
75 m2 = qr.solve(m3);
95 VERIFY_RAISES_ASSERT(qr.solve(tmp))
lu.cpp 94 m2.block(0,0,m2.rows(),m2.cols()) = lu.solve(m3);
124 m2 = lu.solve(m3);
157 VERIFY_RAISES_ASSERT(lu.solve(tmp))
169 VERIFY_RAISES_ASSERT(plu.solve(tmp))
qr_colpivoting.cpp 44 m2 = qr.solve(m3);
69 m2 = qr.solve(m3);
92 m2 = qr.solve(m3);
112 VERIFY_RAISES_ASSERT(qr.solve(tmp))
jacobisvd.cpp 87 SolutionType x = svd.solve(rhs);
159 VERIFY_RAISES_ASSERT(svd.solve(rhs))
167 VERIFY_RAISES_ASSERT(svd.solve(rhs))
174 VERIFY_RAISES_ASSERT(svd.solve(rhs))
179 VERIFY_RAISES_ASSERT(svd.solve(rhs))
203 VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).solve(m), m);
  /external/ceres-solver/internal/ceres/
sparse_normal_cholesky_solver.cc 101 EventLogger event_logger("SparseNormalCholeskySolver::CXSparse::Solve");
121 // Compute the normal equations. J'J delta = J'f and solve them
151 // Solve the linear system.
156 event_logger.AddEvent("Solve");
181 EventLogger event_logger("SparseNormalCholeskySolver::SuiteSparse::Solve");
216 event_logger.AddEvent("Solve");
schur_complement_solver.cc 63 EventLogger event_logger("SchurComplementSolver::Solve");
117 // Solve the system Sx = r, assuming that the matrix S is stored in a
133 // the quality of the solution to the solve.
138 .solve(ConstVectorRef(rhs(), num_rows));
262 // Solve the system Sx = r, assuming that the matrix S is stored in a
317 LOG(WARNING) << "CHOLMOD solve failed.";
335 // Solve the system Sx = r, assuming that the matrix S is stored in a
362 // Solve the linear system.
trust_region_strategy.h 48 // radius, which it then uses to solve for the trust region step using
85 // Per solve options.
126 // Status of the linear solver used to solve the Newton system.
132 // Use the current radius to solve for the trust region step.
  /external/dnsmasq/contrib/openvpn/
README 24 > restarting networking and dnsmasq doesn't solve the problem. Perhaps
32 > My current best idea on how to solve this problem is modifying the
  /external/chromium_org/tools/gyp/test/sibling/
gyptest-all.py 23 # Makefiles (see http://codereview.chromium.org/340008 comments) solve this?
gyptest-relocate.py 25 # Makefiles (see http://codereview.chromium.org/340008 comments) solve this?
  /external/eigen/Eigen/
CholmodSupport 36 #include "src/misc/Solve.h"
PaStiXSupport 38 #include "src/misc/Solve.h"
UmfPackSupport 29 #include "src/misc/Solve.h"
  /external/eigen/Eigen/src/Eigen2Support/
TriangularSolver.h 30 return m_matrix.template triangularView<Added>().solve(other.derived());
  /external/eigen/unsupported/Eigen/
IterativeSolvers 27 #include "../../Eigen/src/misc/Solve.h"
  /external/kernel-headers/original/asm-mips/
msgbuf.h 11 * - extension of time_t to 64-bit on 32-bitsystem to solve the y2038 problem
  /external/kernel-headers/original/asm-x86/
shmbuf.h 10 * - 64-bit time_t to solve y2038 problem
  /external/mockito/src/org/mockito/
Incubating.java 16 * to solve your use case (yes, please, real use cases).</li>
  /prebuilts/gcc/linux-x86/host/i686-linux-glibc2.7-4.4.3/sysroot/usr/include/asm/
shmbuf.h 10 * - 64-bit time_t to solve y2038 problem
  /prebuilts/gcc/linux-x86/host/i686-linux-glibc2.7-4.6/sysroot/usr/include/asm/
shmbuf.h 10 * - 64-bit time_t to solve y2038 problem
  /prebuilts/gcc/linux-x86/host/x86_64-linux-glibc2.7-4.6/sysroot/usr/include/asm/
shmbuf.h 10 * - 64-bit time_t to solve y2038 problem
  /external/eigen/bench/spbench/
spbenchsolver.h 145 x = solver.solve(b);
229 cout<< "SOLVE TIME : " << stat[solver_id].solve_time<< " \n";
248 ostringstream compute,solve,total,error; local
259 solve << markup << stat[i].solve_time;
268 solve << " <TD> -" ;
280 + "<TR><TH>Solve Time " + solve.str() + "\n"
483 * and solve the associated linear system.
484 * The results of each solve are printed in the standard output
  /external/eigen/unsupported/Eigen/src/IterativeSolvers/
GMRES.h 75 VectorType r0 = precond.solve(p0);
104 v=precond.solve(t);
151 // solve upper triangular system
176 VectorType p1=precond.solve(p0);
221 * This class allows to solve for A.x = b sparse linear problems using a generalized minimal
238 * x = solver.solve(b);
241 * // update b, and solve again
242 * x = solver.solve(b);
322 && "GMRES::solve(): invalid number of rows of the right hand side matrix b");
  /external/eigen/doc/
AsciiQuickReference.txt 152 // Solve Ax = b. Result stored in x. Matlab: x = A \ b.
154 solved = A.ldlt().solve(b, &x)); // A sym. p.s.d. #include <Eigen/Cholesky>
155 solved = A.llt() .solve(b, &x)); // A sym. p.d. #include <Eigen/Cholesky>
156 solved = A.lu() .solve(b, &x)); // Stable and fast. #include <Eigen/LU>
157 solved = A.qr() .solve(b, &x)); // No pivoting. #include <Eigen/QR>
158 solved = A.svd() .solve(b, &x)); // Stable, slowest. #include <Eigen/SVD>

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