Uploaded May 2021 | Updated September 2026, 2 weeks ago
In this video tutorial I use the Julia Language to explain the easy steps of the Gram Schmidt process. Following the steps of this process yields a set of orthonormal basis vectors for the (sub) space spanned by the column vectors of a matrix. Each new column vector is orthogonal to all the other column vectors and each is of unit length.
This change of basis is used in QR factorization or QR decomposition of a matrix.
If you want to learn more about the Julia Language take a look at the Playlists here on my channel or earn a certificate from my massive open online course on Coursera coursera.org/learn/julia-programming.
In this video tutorial I use the Julia Language to explain the easy steps of the Gram Schmidt process. Following the steps of this process yields a set of orthonormal basis vectors for the (sub) space spanned by the column vectors of a matrix. Each new column vector is orthogonal to all the other column vectors and each is of unit length.
This change of basis is used in QR factorization or QR decomposition of a matrix.
If you want to learn more about the Julia Language take a look at the Playlists here on my channel or earn a certificate from my massive open online course on Coursera coursera.org/learn/julia-programming.
