Statements (54)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:academic
|
| gptkbp:application |
gptkb:machine_learning
data science engineering scientific computing |
| gptkbp:field |
gptkb:mathematics
|
| gptkbp:focusesOn |
matrix computations
solving linear systems singular value decomposition numerical algorithms eigenvalue problems least squares problems |
| gptkbp:hasSubfield |
randomized algorithms
parallel algorithms preconditioning sparse linear algebra eigenvalue computation matrix equations matrix functions singular value computation structured matrices |
| https://www.w3.org/2000/01/rdf-schema#label |
Numerical linear algebra
|
| gptkbp:notableBook |
Applied Numerical Linear Algebra by James W. Demmel
Numerical Linear Algebra by Lloyd N. Trefethen and David Bau III Matrix Computations by Gene H. Golub and Charles F. Van Loan |
| gptkbp:notableFor |
gptkb:Eigen
gptkb:MATLAB gptkb:NumPy gptkb:SciPy gptkb:BLAS gptkb:LAPACK gptkb:ScaLAPACK |
| gptkbp:relatedTo |
numerical analysis
linear algebra |
| gptkbp:uses |
error analysis
iterative methods Cholesky decomposition LU decomposition QR decomposition parallel computing high performance computing conditioning stability analysis direct methods floating point arithmetic sparse matrices matrix factorizations dense matrices |
| gptkbp:bfsParent |
gptkb:The_Algebraic_Eigenvalue_Problem
gptkb:Nick_Trefethen gptkb:Givens_rotation gptkb:Givens-Rotation gptkb:Numerical_Methods_for_Large_Eigenvalue_Problems |
| gptkbp:bfsLayer |
8
|