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Efficient Approximation of Structured Linear Operators using Randomized One-Sided Queries
Approximating linear operators on high-dimensional spaces using only forward operator-vector product queries is a common but challenging task in scientific computing and machine learning arising when access to the adjoint action is experimentally unavailable or difficult to construct computationally
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