Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10346841 | Computers & Mathematics with Applications | 2005 | 34 Pages |
Abstract
Bivariate cubic L1 splines provide C1-smooth, shape-preserving interpolation of arbitrary data, including data with abrupt changes in spacing and magnitude. The minimization principle for bivariate cubic L1 splines results in a nondifferentiable convex optimization problem. This problem is reformulated as a generalized geometric programming problem. A geometric dual with a linear objective function and convex cubic constraints is derived. A linear system for dual-to-primal conversion is established. The results of computational experiments are presented.
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Computer Science
Computer Science (General)
Authors
Yong Wang, Shu-Cherng Fang, J.E. Lavery, Hao Cheng,