| /external/deqp/external/vulkancts/modules/vulkan/compute/ |
| vktComputeIndirectComputeDispatchTests.cpp | 22 * \brief Indirect Compute Dispatch tests
58 namespace compute namespace in namespace:vkt
244 // Create verify compute shader
253 // Create compute pipeline
280 // Bind compute pipeline
304 // Dispatch indirect compute command
463 // Create compute shader that generates data for indirect buffer
472 // Create compute pipeline
493 // Bind compute pipeline
499 // Dispatch compute comman
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| vktComputeShaderBuiltinVarTests.cpp | 22 * \brief Compute Shader Built-in variable tests.
56 namespace compute namespace in namespace:vkt
463 // Dispatch indirect compute command
564 } // compute
|
| /external/eigen/Eigen/src/Cholesky/ |
| LDLT.h | 31 * \tparam _MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition
75 * perform decompositions via LDLT::compute(const MatrixType&).
112 compute(matrix.derived());
129 compute(matrix.derived());
212 LDLT& compute(const EigenBase<InputType>& matrix);
273 * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
486 /** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
490 LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a) function in class:Eigen::LDLT
499 // Compute matrix L1 norm = max abs column sum.
523 /** Update the LDLT decomposition: given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T
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| LLT.h | 78 * perform decompositions via LLT::compute(const MatrixType&).
96 compute(matrix.derived());
111 compute(matrix.derived());
152 LLT& compute(const EigenBase<InputType>& matrix);
215 * Used to compute and store L
421 LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a) function in class:Eigen::LLT
430 // Compute matrix L1 norm = max abs column sum.
|
| /external/eigen/Eigen/src/CholmodSupport/ |
| CholmodSupport.h | 207 compute(matrix);
232 Derived& compute(const MatrixType& matrix) function in class:Eigen::CholmodBase
287 eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
311 eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
359 eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
448 this->compute(matrix);
499 this->compute(matrix);
548 this->compute(matrix);
599 this->compute(matrix);
|
| /external/eigen/Eigen/src/LU/ |
| FullPivLU.h | 82 * perform decompositions via LU::compute(const MatrixType&).
96 * \param matrix the matrix of which to compute the LU decomposition.
113 * \param matrix the matrix of which to compute the LU decomposition.
119 FullPivLU& compute(const EigenBase<InputType>& matrix) { function in class:Eigen::FullPivLU
472 compute(matrix.derived());
650 /* Thus, all we need to do is to compute Ker U, and then apply Q.
751 * Step 1: compute c = P * rhs.
801 * Step 1: compute c = Q^T rhs.
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| PartialPivLU.h | 96 * perform decompositions via PartialPivLU::compute(const MatrixType&).
110 * \param matrix the matrix of which to compute the LU decomposition.
120 * \param matrix the matrix of which to compute the LU decomposition.
129 PartialPivLU& compute(const EigenBase<InputType>& matrix) { function in class:Eigen::PartialPivLU
131 compute();
229 * Step 1: compute c = Pb.
251 * Step 1: compute c = Pb.
281 void compute();
323 compute(matrix.derived());
336 compute();
515 void PartialPivLU<MatrixType>::compute() function in class:Eigen::PartialPivLU
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| /external/eigen/Eigen/src/QR/ |
| ColPivHouseholderQR.h | 81 * perform decompositions via ColPivHouseholderQR::compute(const MatrixType&).
114 * the method compute(). It is a short cut for:
118 * qr.compute(matrix);
121 * \sa compute()
135 compute(matrix.derived());
211 ColPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
472 ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix) function in class:Eigen::ColPivHouseholderQR
563 // The updated norm has become too inaccurate so re-compute the column
|
| FullPivHouseholderQR.h | 85 * perform decompositions via FullPivHouseholderQR::compute(const MatrixType&).
116 * the method compute(). It is a short cut for:
120 * qr.compute(matrix);
123 * \sa compute()
136 compute(matrix.derived());
195 FullPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
449 FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix) function in class:Eigen::FullPivHouseholderQR
624 // compute the product H'_0 H'_1 ... H'_n-1,
|
| /external/eigen/Eigen/src/SPQRSupport/ |
| SuiteSparseQRSupport.h | 86 compute(matrix);
103 void compute(const _MatrixType& matrix) function in class:Eigen::SPQR
109 /* Compute the default threshold as in MatLab, see:
152 eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()");
155 //Compute Q^T * b
181 eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()");
|
| /external/eigen/Eigen/src/SVD/ |
| BDCSVD.h | 105 * perform decompositions via BDCSVD::compute(const MatrixType&).
136 compute(matrix, computationOptions);
153 BDCSVD& compute(const MatrixType& matrix, unsigned int computationOptions);
159 * This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
161 BDCSVD& compute(const MatrixType& matrix) function in class:Eigen::BDCSVD
163 return compute(matrix, this->m_computationOptions);
237 BDCSVD<MatrixType>& BDCSVD<MatrixType>::compute(const MatrixType& matrix, unsigned int computationOptions) function in class:Eigen::BDCSVD
306 }// end compute
530 // Third part: compute SVD of combined matrix
561 // Compute SVD of m_computed.block(firstCol, firstCol, n + 1, n); this block only has non-zeros i
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| /external/eigen/Eigen/src/SparseCholesky/ |
| SimplicialCholesky.h | 89 derived().compute(matrix);
158 eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
194 void compute(const MatrixType& matrix) function in class:Eigen::SimplicialCholeskyBase
364 SimplicialLLT& compute(const MatrixType& matrix) function in class:Eigen::SimplicialLLT
366 Base::template compute<false>(matrix);
461 SimplicialLDLT& compute(const MatrixType& matrix) function in class:Eigen::SimplicialLDLT
463 Base::template compute<true>(matrix);
523 compute(matrix);
553 SimplicialCholesky& compute(const MatrixType& matrix) function in class:Eigen::SimplicialCholesky
556 Base::template compute<true>(matrix)
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| /external/eigen/Eigen/src/UmfPackSupport/ |
| UmfPackSupport.h | 15 /* TODO extract L, extract U, compute det, etc... */
171 compute(matrix);
223 void compute(const InputMatrixType& matrix) function in class:Eigen::UmfPackLU
236 * \sa factorize(), compute()
252 * \sa factorize(), compute()
286 * \sa analyzePattern(), compute()
|
| /external/guava/guava/src/com/google/common/collect/ |
| MapMaker.java | 573 * @param computingFunction the function used to compute new values
851 private V compute(K key) { method in class:MapMaker.NullComputingConcurrentMap
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| /external/libvorbis/doc/ |
| 01-introduction.tex | 301 \item compute dot product of floor and residue, producing audio spectrum vector
447 \paragraph{compute floor/residue dot product}
|
| /libcore/luni/src/test/java/libcore/java/util/ |
| MapDefaultMethodTester.java | 375 assertEquals(5.0, m.compute(1, (k, v) -> 5.0));
380 assertEquals(11.0, m.compute(1, (k, v) -> k + v));
384 assertNull(m.compute(1, (k, v) -> null));
389 m.compute(1, null);
394 assertEquals(10.0, m.compute(null, (k, v) -> 10.0));
398 m.compute(null, (k, v) -> 5.0);
|
| /prebuilts/tools/common/m2/repository/com/google/dagger/dagger-producers/2.0-beta/ |
| dagger-producers-2.0-beta.jar | |
| /external/clang/utils/TableGen/ |
| ClangDiagnosticsEmitter.cpp | 313 /// Compute the set of diagnostics and groups that are immediately
315 void compute(VecOrSet DiagsInPedantic,
367 // Lazily compute the threshold value for the group count.
394 void InferPedantic::compute(VecOrSet DiagsInPedantic, function in class:InferPedantic
412 // Compute the set of diagnostics that are directly in -Wpedantic. We
438 // Compute the set of groups that are directly in -Wpedantic. We
508 // Compute the set of diagnostics that are in -Wpedantic.
511 inferPedantic.compute(&DiagsInPedantic, (RecordVec*)nullptr);
825 // Compute a mapping from a DiagGroup to all of its parents.
842 inferPedantic.compute(&DiagsInPedantic, &GroupsInPedantic)
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| /external/eigen/Eigen/src/Eigenvalues/ |
| RealQZ.h | 38 * Call the function compute() to compute the real QZ decomposition of a
80 * perform decompositions via compute(). The \p size parameter is only
84 * \sa compute() for an example.
102 * This constructor calls compute() to compute the QZ decomposition.
112 compute(A, B, computeQZ);
160 RealQZ& compute(const MatrixType& A, const MatrixType& B, bool computeQZ = true);
446 // Compute the shifts: (x,y,z,0...) = (AB^-1 - l1 I) (AB^-1 - l2 I) e1
556 RealQZ<MatrixType>& RealQZ<MatrixType>::compute(const MatrixType& A_in, const MatrixType& B_in, bool computeQZ function in class:Eigen::RealQZ
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| SelfAdjointEigenSolver.h | 55 * Call the function compute() to compute the eigenvalues and eigenvectors of
110 * perform decompositions via compute(). This constructor
131 * intends to perform decompositions via compute(). The \p size
135 * \sa compute() for an example
151 * This constructor calls compute(const MatrixType&, int) to compute the
158 * \sa compute(const MatrixType&, int)
168 compute(matrix.derived(), options);
203 SelfAdjointEigenSolver& compute(const EigenBase<InputType>& matrix, int options = ComputeEigenvectors)
400 ::compute(const EigenBase<InputType>& a_matrix, int options) function in class:Eigen::SelfAdjointEigenSolver
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| /external/eigen/Eigen/src/SparseLU/ |
| SparseLU.h | 49 * // Compute the ordering permutation vector from the structural pattern of A
51 * // Compute the numerical factorization
108 compute(matrix);
121 * Compute the symbolic and numeric factorization of the input sparse matrix.
124 void compute (const MatrixType& matrix) function in class:Eigen::SparseLU
188 * \sa compute()
401 * Compute the column permutation to minimize the fill-in
405 * - Compute the column elimination tree on the permuted matrix
414 //TODO It is possible as in SuperLU to compute row and columns scaling vectors to equilibrate the matrix mat.
419 // Compute fill-in orderin
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| /external/eigen/Eigen/src/SparseQR/ |
| SparseQR.h | 101 * \sa compute()
105 compute(mat);
114 void compute(const MatrixType& mat) function in class:Eigen::SparseQR
151 eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
194 eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
199 // Compute Q^T * b;
230 * \sa compute()
235 eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
242 eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
312 // Compute the column fill reducing orderin
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| /external/eigen/Eigen/src/SuperLUSupport/ |
| SuperLUSupport.h | 365 void compute(const MatrixType& matrix) function in class:Eigen::SuperLUBase
511 Base::compute(matrix);
651 eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
709 eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for extracting factors, you must first call either compute() or analyzePattern()/factorize()");
795 eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for computing the determinant, you must first call either compute() or analyzePattern()/factorize()");
858 Base::compute(matrix);
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| /external/eigen/unsupported/Eigen/src/MatrixFunctions/ |
| MatrixFunction.h | 38 * \param[in] f matrix function to compute.
42 /** \brief Compute matrix function of atomic matrix
46 MatrixType compute(const MatrixType& A);
64 MatrixType MatrixFunctionAtomic<MatrixType>::compute(const MatrixType& A) function in class:Eigen::internal::MatrixFunctionAtomic
163 /** \brief Compute size of each cluster given a partitioning */
176 /** \brief Compute start of each block using clusterSize */
187 /** \brief Compute mapping of eigenvalue indices to cluster indices */
204 /** \brief Compute permutation which groups ei'vals in same cluster together */
240 /** \brief Compute block diagonal part of matrix function.
252 = atomic.compute(T.block(blockStart(i), blockStart(i), clusterSize(i), clusterSize(i)))
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| /external/llvm/lib/Analysis/ |
| MemoryBuiltins.cpp | 367 // Utility functions to compute size of objects.
375 /// \brief Compute the size of the object pointed by Ptr. Returns true and the
384 SizeOffsetType Data = Visitor.compute(const_cast<Value*>(Ptr));
414 SizeOffsetType ObjectSizeOffsetVisitor::compute(Value *V) { function in class:ObjectSizeOffsetVisitor
446 DEBUG(dbgs() << "ObjectSizeOffsetVisitor::compute() unhandled value: " << *V
569 SizeOffsetType PtrData = compute(GEP.getPointerOperand());
580 return compute(GA.getAliasee());
607 SizeOffsetType TrueSide = compute(I.getTrueValue());
608 SizeOffsetType FalseSide = compute(I.getFalseValue());
648 // IntTy and Zero must be set for each compute() since the address space ma
652 SizeOffsetEvalType ObjectSizeOffsetEvaluator::compute(Value *V) { function in class:ObjectSizeOffsetEvaluator
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