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Medium · Linear Algebra & Machine Learning · Quant Trader interview question · linear-algebra, gram-schmidt, orthogonalization, computational-complexity
You are developing a high-frequency trading algorithm that relies on rapidly orthogonalizing a set of feature vectors. The Gram-Schmidt process is a candidate method. Given a set of $n$ linearly independent vectors in $R^m$, describe the Gram-Schmidt process for orthogonalizing this set of vectors. What is the computational complexity of the Gram-Schmidt process for $n$ vectors in $R^m$?