4,787 research outputs found
A Riemannian View on Shape Optimization
Shape optimization based on the shape calculus is numerically mostly
performed by means of steepest descent methods. This paper provides a novel
framework to analyze shape-Newton optimization methods by exploiting a
Riemannian perspective. A Riemannian shape Hessian is defined yielding often
sought properties like symmetry and quadratic convergence for Newton
optimization methods.Comment: 15 pages, 1 figure, 1 table. Forschungsbericht / Universit\"at Trier,
Mathematik, Informatik 2012,
Towards a Lagrange-Newton approach for PDE constrained shape optimization
The novel Riemannian view on shape optimization developed in [Schulz, FoCM,
2014] is extended to a Lagrange-Newton approach for PDE constrained shape
optimization problems. The extension is based on optimization on Riemannian
vector space bundles and exemplified for a simple numerical example.Comment: 16 pages, 4 figures, 1 tabl
Parametric Regression on the Grassmannian
We address the problem of fitting parametric curves on the Grassmann manifold
for the purpose of intrinsic parametric regression. As customary in the
literature, we start from the energy minimization formulation of linear
least-squares in Euclidean spaces and generalize this concept to general
nonflat Riemannian manifolds, following an optimal-control point of view. We
then specialize this idea to the Grassmann manifold and demonstrate that it
yields a simple, extensible and easy-to-implement solution to the parametric
regression problem. In fact, it allows us to extend the basic geodesic model to
(1) a time-warped variant and (2) cubic splines. We demonstrate the utility of
the proposed solution on different vision problems, such as shape regression as
a function of age, traffic-speed estimation and crowd-counting from
surveillance video clips. Most notably, these problems can be conveniently
solved within the same framework without any specifically-tailored steps along
the processing pipeline.Comment: 14 pages, 11 figure
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