QR factorization is a mathematical method that decomposes a matrix into two components: an orthogonal matrix Q and an upper triangular matrix R. This technique is essential for solving linear systems, least squares problems, and in numerical linear algebra applications, providing stability and efficiency. The connection between the matrices Q and R plays a crucial role in optimization problems often encountered in inverse problems.
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