Multi-parameter regularization is a technique used in inverse problems to stabilize the solution when dealing with ill-posed or non-linear problems by introducing multiple regularization parameters. This method allows for the adjustment of various factors that influence the model, improving its ability to approximate true solutions under different scenarios. It is particularly useful in managing trade-offs between fitting the data and controlling model complexity, making it a vital tool in handling uncertainty and noise in data.
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