 Numerical Algorithms for Non-smooth Optimization Applicable to Seismic RecoveryIgnace Loris ()
A chapter in Handbook of Geomathematics, 2015, pp 1905-1942 from Springer Abstract: Abstract Inverse problems in seismic tomography are often cast in the form of an optimization problem involving a cost function composed of a data misfit term and regularizing constraint or penalty. Depending on the noise model that is assumed to underlie the data acquisition, these optimization problems may be non-smooth. Another source of lack of smoothness (differentiability) of the cost function may arise from the regularization method chosen to handle the ill-posed nature of the inverse problem. A numerical algorithm that is well suited to handle minimization problems involving two non-smooth convex functions and two linear operators is studied. The emphasis lies on the use of some simple proximity operators that allow for the iterative solution of non-smooth convex optimization problems. Explicit formulas for several of these proximity operators are given and their application to seismic tomography is demonstrated. Keywords: Proximal Operator; Data Misfit; Global Seismic Tomography; Uniform Noise Model; Linear Inverse Problems (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-54551-1_65 Ordering information: This item can be ordered from DOI: 10.1007/978-3-642-54551-1_65 Access Statistics for this chapter More chapters in Springer Books from Springer |
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