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What is Iterative Methods for Least-Squares Solutions about?
Iterative methods like steepest descent and conjugate gradients can be used to solve large least squares problems more efficiently than directly computing the matrix inverse. Steepest descent works by iteratively moving in the direction of steepest descent, the negative gradient, to minimize a convex objective function. Each iteration requires applying the matrix A and its transpose AT once. If the matrix is well-conditioned, these methods can converge in few iterations, making the cost much lower than dire
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- uranub2787
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- EN