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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, biologicalobjects, 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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