Iterative regularization methods are techniques used to stabilize the solutions of inverse problems by iteratively refining an initial guess while incorporating regularization principles. These methods aim to mitigate issues like noise and ill-posedness in the data, producing more reliable and stable solutions through a series of updates. By progressively improving the solution based on available information, they strike a balance between fitting the data and maintaining a level of smoothness or other desired properties in the solution.
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