Symes, William W.
Extremal regularization finds a model fitting the data to a specified tolerance, and additionally minimizing an auxiliary criterion. It provides relative model/data space weights when no statistical information about the model or data is available other than an estimate of noise level. A version of the Moré-Hebden algorithm using conjugate gradients to solve the various linear systems implements extremal regularization for large scale inverse problems. A deconvolution application illustrates the possibilities and pitfalls of extremal regularization in the linear case.
Citable link to this pagehttps://hdl.handle.net/1911/101913
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- CAAM Technical Reports