On model-space and data-space regularization: A tutorial
نویسنده
چکیده
Constraining ill-posed inverse problems often requires regularized optimization. I describe two alternative approaches to regularization. The first approach involves a column operator and an extension of the data space. The second approach constructs a row operator and expands the model space. In large-scale problems, when the optimization is incomplete, the two methods of regularization behave differently. I illustrate this fact with simple examples and discuss its implications for geophysical problems.
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