2 research outputs found
A global linear and local superlinear/quadratic inexact non-interior continuation method for variational inequalities
We use the concept of barrier-based smoothing approximations introduced in [
C. B. Chua and Z. Li, A barrier-based smoothing proximal point algorithm for
NCPs over closed convex cones, SIOPT 23(2), 2010] to extend the non-interior
continuation method proposed in [B. Chen and N. Xiu, A global linear and local
quadratic noninterior continuation method for nonlinear complementarity
problems based on Chen-Mangasarian smoothing functions, SIOPT 9(3), 1999] to an
inexact non-interior continuation method for variational inequalities over
general closed convex sets. Newton equations involved in the method are solved
inexactly to deal with high dimension problems. The method is proved to have
global linear and local superlinear/quadratic convergence under suitable
assumptions. We apply the method to non-negative orthants, positive
semidefinite cones, polyhedral sets, epigraphs of matrix operator norm cone and
epigraphs of matrix nuclear norm cone
Approximate Program Smoothing Using Mean-Variance Statistics, with Application to Procedural Shader Bandlimiting
This paper introduces a general method to approximate the convolution of an
arbitrary program with a Gaussian kernel. This process has the effect of
smoothing out a program. Our compiler framework models intermediate values in
the program as random variables, by using mean and variance statistics. Our
approach breaks the input program into parts and relates the statistics of the
different parts, under the smoothing process. We give several approximations
that can be used for the different parts of the program. These include the
approximation of Dorn et al., a novel adaptive Gaussian approximation, Monte
Carlo sampling, and compactly supported kernels. Our adaptive Gaussian
approximation is accurate up to the second order in the standard deviation of
the smoothing kernel, and mathematically smooth. We show how to construct a
compiler that applies chosen approximations to given parts of the input
program. Because each expression can have multiple approximation choices, we
use a genetic search to automatically select the best approximations. We apply
this framework to the problem of automatically bandlimiting procedural shader
programs. We evaluate our method on a variety of complex shaders, including
shaders with parallax mapping, animation, and spatially varying statistics. The
resulting smoothed shader programs outperform previous approaches both
numerically, and aesthetically, due to the smoothing properties of our
approximations.Comment: 13 pages, 6 figure