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The Gram–Schmidt process can be stabilized by a small modification; this version is sometimes referred to as modified Gram-Schmidt or MGS. This approach gives the same result as the original formula in exact arithmetic and introduces smaller errors in finite-precision arithmetic.
We could use an orthogonal projection (Gram-Schmidt) but this will be numerically unstable if the vectors and are close to orthogonal. Instead, the Householder reflection reflects through the dotted line (chosen to bisect the angle between x {\displaystyle \mathbf {x} } and e 1 {\displaystyle \mathbf {e} _{1}} ).
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
For example, the sine functions ... and applies the Gram–Schmidt process, then one obtains the Legendre polynomials. Another collection of orthogonal polynomials ...
Using Zorn's lemma and the Gram–Schmidt process (or more simply well-ordering and transfinite recursion), one can show that every Hilbert space admits an orthonormal basis; [7] furthermore, any two orthonormal bases of the same space have the same cardinality (this can be proven in a manner akin to that of the proof of the usual dimension ...
For degenerate eigenfunctions with the same eigenvalue λ i, orthogonal eigenfunctions can always be chosen that span the eigenspace associated with λ i, for example by using the Gram-Schmidt process. [5]
An alternative way to arrive at the same expressions is to take the first three derivatives of the curve r′(t), r′′(t), r′′′(t), and to apply the Gram-Schmidt process. The resulting ordered orthonormal basis is precisely the TNB frame. This procedure also generalizes to produce Frenet frames in higher dimensions.
A Gram–Schmidt process could orthogonalize the columns, but it is not the most reliable, nor the most efficient, nor the most invariant method. The polar decomposition factors a matrix into a pair, one of which is the unique closest orthogonal matrix to the given matrix, or one of the closest if the given matrix is singular.