Structured random sketching for PDE inverse problems
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For an overdetermined system Ax≈b with A and b given, the least-square (LS) formulation _x Ax-b_2 is often used to find an acceptable solution x. The cost of solving this problem depends on the dimensions of A, which are large in many practical instances. This cost can be reduced by the use of random sketching, in which we choose a matrix S with fewer rows than A and b, and solve the sketched LS problem _x S(Ax-b)_2 to obtain an approximate solution to the original LS problem. Significant theoretical and practical progress has been made in the last decade in designing the appropriate structure and distribution for the sketching matrix S. When A and b arise from discretizations of a PDE-based inverse problem, tensor structure is often present in A and b. For reasons of practical efficiency, S should be designed to have a structure consistent with that of A. Can we claim similar approximation properties for the solution of the sketched LS problem with structured S as for fully-random S? We give estimates that relate the quality of the solution of the sketched LS problem to the size of the structured sketching matrices, for two different structures. Our results are among the first known for random sketching matrices whose structure is suitable for use in PDE inverse problems.
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