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The Gram matrix is symmetric in the case the inner product is real-valued; it is Hermitian in the general, complex case by definition of an inner product. The Gram matrix is positive semidefinite, and every positive semidefinite matrix is the Gramian matrix for some set of vectors. The fact that the Gramian matrix is positive-semidefinite can ...
This makes only the Gram–Schmidt process applicable for iterative methods like the Arnoldi iteration. Yet another alternative is motivated by the use of Cholesky decomposition for inverting the matrix of the normal equations in linear least squares. Let be a full column rank matrix, whose columns need to be orthogonalized.
The Gram matrix of a sequence of points ,, …, in k-dimensional space ℝ k is the n×n matrix = of their dot products (here a point is thought of as a vector from 0 to that point): g i j = x i ⋅ x j = ‖ x i ‖ ‖ x j ‖ cos θ {\displaystyle g_{ij}=x_{i}\cdot x_{j}=\|x_{i}\|\|x_{j}\|\cos \theta } , where θ {\displaystyle \theta ...
In linear algebra, the Cholesky decomposition or Cholesky factorization (pronounced / ʃ ə ˈ l ɛ s k i / shə-LES-kee) is a decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose, which is useful for efficient numerical solutions, e.g., Monte Carlo simulations.
In mathematics, the Iwasawa decomposition (aka KAN from its expression) of a semisimple Lie group generalises the way a square real matrix can be written as a product of an orthogonal matrix and an upper triangular matrix (QR decomposition, a consequence of Gram–Schmidt orthogonalization).
For many problems in applied linear algebra, it is useful to adopt the perspective of a matrix as being a concatenation of column vectors. For example, when solving the linear system =, rather than understanding x as the product of with b, it is helpful to think of x as the vector of coefficients in the linear expansion of b in the basis formed by the columns of A.
Magma as the functions LLL and LLLGram (taking a gram matrix) Maple as the function IntegerRelations[LLL] Mathematica as the function LatticeReduce; Number Theory Library (NTL) as the function LLL; PARI/GP as the function qflll; Pymatgen as the function analysis.get_lll_reduced_lattice; SageMath as the method LLL driven by fpLLL and NTL
Linear Time Invariant (LTI) Systems are those systems in which the parameters , , and are invariant with respect to time. One can observe if the LTI system is or is not controllable simply by looking at the pair ( A , B ) {\displaystyle ({\boldsymbol {A}},{\boldsymbol {B}})} .