Piecewise Differentiable Minimization for Ill-posed Inverse Problems
نویسندگان
چکیده
Based on minimizing a piecewise differentiable lp function subject to a single inequality constraint, this paper discusses algorithms for a discretized regularization problem for ill-posed inverse problems. We examine computational challenges of solving this regularization problem. Possible minimization algorithms such as the steepest descent method, iteratively weighted least squares (IRLS) method and a recent globally convergent affine scaling Newton approach are considered. Limitations and efficiency of these algorithms are demonstrated using the geophysical traveltime tomographic inversion and image restoration applications.
منابع مشابه
Piecewise Differentiable Minimization for Ill-posed Inverse Problems Ing from the National Science Foundation and Ibm Corporation, with Additional Support from New York State and Members of Its Corporate Research Institute. 1
Based on minimizing a piecewise diierentiable lp function subject to a single inequality constraint, this paper discusses algorithms for a discretized regularization problem for ill-posed inverse problems. We examine computational challenges of solving this regularization problem. Possible minimization algorithms such as the steepest descent method, iteratively weighted least squares (IRLS) met...
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