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Title page for ETD etd-04082005-144213


Type of Document Dissertation
Author Wang, Yong ,
Author's Email Address yongwang.yw@gmail.com
URN etd-04082005-144213
Title Theory and algorithms for shape-preserving bivariate cubic L1 splines.
Degree PhD
Graduate Program Operations Research
Advisory Committee
Advisor Name Title
Shu-Cherng Fang Committee Chair
Keywords
  • spline function
  • tensor-product
  • cubic L1 spline
  • interpolation
  • geometric programming
  • primal-dual method
Date of Defense 2005-03-28
Availability unrestricted
Abstract
A major objective of modelling geophysical features, biological

objects, financial processes and many other irregular surfaces and

functions is to develop "shape-preserving" methodologies for

smoothly interpolating bivariate data with sudden changes in

magnitude or spacing. Shape preservation usually means the

elimination of extraneous non-physical oscillations. Classical

splines do not preserve shape well in this sense.

Empirical experiments have shown that the recently proposed cubic

L1 splines are cable of providing C1-smooth,

shape-preserving, multi-scale interpolation of arbitrary data,

including data with abrupt changes in spacing and magnitude, with

no need for node adjustment or other user input. However, a

theoretic treatment of the bivariate cubic L1 splines is still

lacking. The currently available approximation algorithms are not

able to generate the exact coefficients of a bivariate cubic L1

spline.

For theoretical treatment and the algorithm development, we

propose to solve bivariate cubic L1 spline problems in a

generalized geometric programming framework. Our framework

includes a primal problem, a geometric dual problem with a linear

objective function and convex cubic constraints, and a linear

system for dual-to-primal transformation. We show that bivariate

cubic L1 splines indeed preserve linearity under some mild

conditions.

Since solving the dual geometric program involves heavy

computation, to improve computational efficiency, we further

develop three methods for generating bivariate cubic L1

splines: a tensor-product approach that generates a good

approximation for large scale bivariate cubic L1 splines; a

primal-dual interior point method that obtains discretized

bivariate cubic L1 splines robustly for small and medium size

problems; and a compressed primal-dual method that efficiently and

robustly generates discretized bivariate cubic L1 splines of

large size.

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