Opening the book…
Implement the Gram-Schmidt orthoganalization process from the Hefron textbook (page 282). This function takes a \(m \times n\) Matrix \(A\) with linearly independent columns as input and return a \(m \times n\) Matrix \(G\) with orthogonal column vectors. The basic algorithm works as follows:
AT = transpose(A)(this process works with the columns of the matrix so it is easier to work with the transpose. Think about a list of list, it is easy to get a row (a list)).- Make a new empty list of the same size as
ATand call itGT(G transpose) - Loop index
iover all of the rows in AT (i.e. columns of A)GT[i] = AT[i]- Loop index
jfrom 0 toiGT[i] -= proj(GT[i], GT[j])
G = transpose(GT)
Use the following function definition as a template:
Here, we are going to test your function using the vectors:
What is the Big-O complexity of the Gram-Schmidt process?
This page titled 26.3: Gram-Schmidt Orthogonalization Process was last modified on Sat, 17 Sep 2022 15:14:22 GMT and is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by Dirk Colbry via source content that was edited to the style and standards of the LibreTexts platform.
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