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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.
Thus the problem of finding conjugate axes is less constrained than the problem of orthogonalization, so the Gram–Schmidt process works, with additional degrees of freedom that we can later use to pick the ones that would simplify the computation: Arbitrarily set .
$ tex Gram-Schmidt_process.tex && dvips -E Gram-Schmidt_process.dvi; Outline fonts $ eps2eps -dNOCACHE Gram-Schmidt_process.ps Gram-Schmidt_process2.eps; Fix bounding box $ ps2epsi Gram-Schmidt_process2.eps Gram-Schmidt_process.eps; Convert to Sketch $ pstoedit -f sk Gram-Schmidt_process.eps Gram-Schmidt_process.sk; Convert to SVG
In linear algebra, the Schmidt decomposition (named after its originator Erhard Schmidt) refers to a particular way of expressing a vector in the tensor product of two inner product spaces. It has numerous applications in quantum information theory , for example in entanglement characterization and in state purification , and plasticity .
1 Gram-Schmidt orthonormalization process. Toggle the table of contents. Wikipedia: Featured picture candidates/Gram-Schmidt orthonormalization process. Add languages.
Jørgen Pedersen Gram (27 June 1850 – 29 April 1916) was a Danish actuary and mathematician who was born in Nustrup, Duchy of Schleswig, Denmark and died in Copenhagen, Denmark. Important papers of his include On series expansions determined by the methods of least squares , and Investigations of the number of primes less than a given number .
More generally, we can factor a complex m×n matrix A, with m ≥ n, as the product of an m×m unitary matrix Q and an m×n upper triangular matrix R.As the bottom (m−n) rows of an m×n upper triangular matrix consist entirely of zeroes, it is often useful to partition R, or both R and Q:
In numerical linear algebra, the Arnoldi iteration is an eigenvalue algorithm and an important example of an iterative method.Arnoldi finds an approximation to the eigenvalues and eigenvectors of general (possibly non-Hermitian) matrices by constructing an orthonormal basis of the Krylov subspace, which makes it particularly useful when dealing with large sparse matrices.