The Gram-Schmidt process is a method for constructing an orthonormal basis from a set of vectors in an inner product space, most commonly Euclidean space with the standard inner product. It takes a finite, linearly independent set of vectors and generates an orthogonal set spanning the same subspace, and it is named after Jorgen Pedersen Gram and Erhard Schmidt, though Pierre-Simon Laplace had been familiar with the method earlier; applying it to the columns of a full column rank matrix yields the QR decomposition.
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Gram-Schmidt process (Wikipedia)
In Field: Algorithms and Complexity Theory, Lead sentenceQuote, In Field: Algorithms and Complexity Theory, Lead sentence
Gram-Schmidt process or Gram-Schmidt algorithm is a way of finding a set of two or more vectors that are perpendicular to each other.
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