### Decomposition and Solve documentation

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 --- layout: post title: "Decompositions and Linear Equations" title: "Decompositions and Solve" date: 2000-11-26 topic: "Basic Usage" section: "Documentation" --- __tabsInit # Decompositions and Linear Equations # Decompositions and Solve In matrix calculus the decomposition of a matrix into the matrix product of several matrices with special properties (eg. into an orthogonal and a triangular matrix (QR) or orthogonal matrices and diagonal of singular values (SVD)) are among the most powerful tools to devise numerical algorithms. In the case of tensors of higher degree it is necessary to indicate along which modes the decomposition is supposed to happen, so `xerus` uses the notation of indexed equations explained in the previous chapter ([Indices and Equations](indices)). ## QR Decompositions To provide an intuitive approach to decompositions, `xerus` uses the assignment of multiple tensors with a single operator to denote them. Here `(Q, R) = QR(A)` reads as "Q and R are defined as the QR-Decomposition of A", even though we naturally have to provide indices to make this line well defined: __tabsStart ~~~ cpp // perform QR decomposition of A and store result in Q and R (Q(i,r), R(r,j)) = xerus.QR(A(i,j)); ~~~ __tabsMid ~~~ py # perform QR decomposition of A and store result in Q and R (Q(i,r), R(r,j)) << xerus.QR(A(i,j)) ~~~ __tabsEnd In these decompositions we can distribute the modes of the original tensor as we please, but to be well defined, `Q` and `R` must only share one and exactly one index. __tabsStart ~~~ cpp // well defined QR decomposition of a degree 4 tensor (Q(i,r,k), R(l,r,j)) = xerus.QR(A(i,j,k,l)); // invalid: (Q(i,r,s), R(r,s,j)) = xerus.QR(A(i,j)); ~~~ __tabsMid ~~~ py # well defined QR decomposition of a degree 4 tensor (Q(i,r,k), R(l,r,j)) << xerus.QR(A(i,j,k,l)) # invalid: (Q(i,r,s), R(r,s,j)) << xerus.QR(A(i,j)) ~~~ __tabsEnd For convenience `xerus` defines four variants of the QR decomposition. Assuming the input is of size \$m\times n\$, \$min = \operatorname{min}(m,n)\$ and \$r\$ the rank of the input we have following resulting objects:
Left-Hand-Side Right-Hand-Side
Decomposition Property Dimension Property Dimension
xerus.QR orthogonal \$m\times min\$ upper triangular \$min\times n\$
xerus.RQ upper triangular \$m\times min\$ orthogonal \$min\times n\$
xerus.QC orthogonal \$m\times r\$ upper or lower triangular \$r\times n\$
xerus.CQ upper or lower triangular \$m\times r\$ orthogonal \$r\times n\$
## Singular Value Decompositions The Singular Value Decomposition in `xerus` is called very much like the `QR` decomposition: __tabsStart ~~~ cpp // calculate the SVD of A and store the resulting matrices in U, S and Vt (U(i,r1), S(r1,r2), Vt(r2,j)) = xerus.SVD(A(i,j)); ~~~ __tabsMid ~~~ py # calculate the SVD of A and store the resulting matrices in U, S and Vt (U(i,r1), S(r1,r2), Vt(r2,j)) << xerus.SVD(A(i,j)) ~~~ __tabsEnd In this form it is rank-revealing (so `S` is of dimensions \$r\times r\$ instead of \$\operatorname{min}(m,n)\times\operatorname{min}(m,n)\$) and exact, but it is possible to pass optional arguments to use it as a truncated SVD. __tabsStart ~~~ cpp // calculate the SVD, truncated to at most 5 singular values size_t numSingularVectors = 5; // or until a singular value is smaller than 0.01 times the maximal singular value double epsilon = 0.01; (U(i,r1), S(r1,r2), Vt(r2,j)) = xerus.SVD(A(i,j), numSingularVectors, epsilon); ~~~ __tabsMid ~~~ py # calculate the SVD, truncated to at most 5 singular values numSingularVectors = 5 # or until a singular value is smaller than 0.01 times the maximal singular value epsilon = 0.01 (U(i,r1), S(r1,r2), Vt(r2,j)) = xerus.SVD(A(i,j), maxRank=numSingularVectors, eps=epsilon); ~~~ __tabsEnd ## Solving Linear Equations A common application for matrix decompositions is to solve matrix equations of the form \$A\cdot x = b\$ for \$x\$ via QR, LU, LDL\$^T\$ or Cholesky decompositions. In xerus this is again provided in indexed notation via the `operator/`. __tabsStart ~~~ cpp // solve A(i,j)*x(j) = b(i) for x x(j) = b(i) / A(i,j); ~~~ __tabsMid ~~~ py # solve A(i,j)*x(j) = b(i) for x x(j) << b(i) / A(i,j) ~~~ __tabsEnd Depending on the representation and some properties of `A`, `xerus` will automatically choose one of the above mentioned decompositions to solve the system.
 ... ... @@ -35,10 +35,13 @@ void expose_factorizations() { }) ; class_, boost::noncopyable>("SVD_temporary", boost::python::no_init); def("SVD", +[](IndexedTensor &_rhs)->TensorFactorisation*{ return new SVD(std::move(_rhs)); def("SVD", +[](IndexedTensor &_rhs, size_t _maxRank, double _eps)->TensorFactorisation*{ return new SVD(std::move(_rhs), _maxRank, _eps); }, return_value_policy>()); // but the argument will not be destroyed before the result is destroyed with_custodian_and_ward_postcall<0,1>>(), // but the argument will not be destroyed before the result is destroyed (arg("source"), arg("maxRank")=std::numeric_limits::max(), arg("eps")=EPSILON) ); class_, boost::noncopyable>("QR_temporary", boost::python::no_init); def("QR", +[](IndexedTensor &_rhs)->TensorFactorisation*{ ... ... @@ -64,12 +67,4 @@ void expose_factorizations() { }, return_value_policy>()); // but the argument will not be destroyed before the result is destroyed enum_("Representation", "Possible representations of Tensor objects.") .value("Dense", Tensor::Representation::Dense) .value("Sparse", Tensor::Representation::Sparse) ; enum_("Initialisation", "Possible initialisations of new Tensor objects.") .value("Zero", Tensor::Initialisation::Zero) .value("None", Tensor::Initialisation::None) ; }
 ... ... @@ -26,6 +26,15 @@ #include "misc.h" void expose_tensor() { enum_("Representation", "Possible representations of Tensor objects.") .value("Dense", Tensor::Representation::Dense) .value("Sparse", Tensor::Representation::Sparse) ; enum_("Initialisation", "Possible initialisations of new Tensor objects.") .value("Zero", Tensor::Initialisation::Zero) .value("None", Tensor::Initialisation::None) ; { scope Tensor_scope = class_("Tensor", "a non-decomposed Tensor in either sparse or dense representation" ... ...
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