Efficient methods for large‐scale linear inversion using a geostatistical approach

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Large‐scale hydraulic tomography and joint inversion of head and tracer data using the Principal Component Geostatistical Approach (PCGA) (2014, methodology): “…of Q. Combined with fast matrix linear algebra methods such FFT [Nowak et al., 2003], the fast-multipole method [Greengard and Rokhlin, 1987; Fong and Darve, 2009] or hierachical matrices [Ambikasaran et al., 2013; Saibaba and Kitanidis, 2012], one can achieve a computational cost of OðmK log mÞ.”
Likelihood approximation with hierarchical matrices for large spatial datasets (2017, result): “Previous results (Hackbusch, 2015) show that the H-matrix technique is very stable when approximating the matrix itself (Li et al., 2014; Saibaba et al., 2012; Ballani and Kressner, 2015; Harbrecht et al., 2015; Ambikasaran et al., 2013; Börm and Garcke, 2007; Saibaba and Kitanidis, 2012), its inver”