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This paper introduces a new strategy for setting the regularization parameterwhen solving large-scale discrete ill-posed linear problems by means of theArnoldi-Tikhonov method. This new rule is essentially based on the discrepancyprinciple, although no initial knowledge of the norm of the error that affectsthe right-hand side is assumed; an increasingly more accurate approximation ofthis quantity is recovered during the Arnoldi algorithm. Some theoreticalestimates are derived in order to motivate our approach. Many numericalexperiments, performed on classical test problems as well as image deblurringare presented.

Embedded techniques for choosing the parameter in Tikhonov regularization