Minimization in Krylov spaces refers to the process of finding an approximate solution to a linear system or an optimization problem by utilizing a sequence of subspaces generated from the action of a matrix on a vector. This approach leverages the properties of Krylov subspaces to efficiently approximate solutions with reduced computational complexity, making it particularly useful in iterative methods for solving large-scale problems.
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