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Sequential predictor-corrector methods for the variable regularization of Volterra inverse problems
2000
Inverse Problems
We analyze the convergence of a class of discrete predictor-corrector methods for the sequential regularization of first-kind Volterra integral equations. In contrast to classical methods such as Tikhonov regularization, this class of methods preserves the Volterra (causal) structure of the original problem. The result is a discretized regularization method for which the number of arithmetic operations is O(N 2 ) (where N is the dimension of the approximating space) in contrast to standard
doi:10.1088/0266-5611/16/2/308
fatcat:46xj3tmjgfhppeeibbspr7htqi