We consider the problem of reconstructing an image from noisy and/or incomplete data. The
values of the pixels lie on a Riemannian manifold $M$ , eg $\mathbb {R}$ for a grayscale …

We introduce a novel type of approximation spaces for functions with values in a nonlinear
manifold. The discrete functions are constructed by piecewise polynomial interpolation in a …

Unique-sink orientations (USOs) are an abstract class of orientations of the n -cube graph.
We consider some classes of USOs that are of interest in connection with the linear …

We consider the problem of approximating manifold-valued functions with approximation
spaces spanned by linear combinations of cardinal B-splines with control points constrained to …

We consider the problem of approximating a function f from an Euclidean domain to a manifold
M by scattered samples $$(f(\xi _i))_{i\in \mathcal {I}}$$ ( f ( ξ i ) ) i ∈ I , where the data …

We consider the problem of reconstructing an image from noisy and/or incomplete data, where
the image/data take values in a metric space X (eg R for grayscale, S2 for the chromaticity …

In this thesis, we consider optimization and variational problems where the data is constrained
to lie on a Riemannian manifold. Two examples, we will particularly focus on, are the …

We give the first polynomial-time algorithm for solving the linear complementarity problem
with tridiagonal or, more generally, Hessenberg P-matrices.

We consider approximations of functions from samples where the functions take values on a
submanifold of Rn. We generalize a common quasiinterpolation scheme based on cardinal …

We consider the problem of numerically computing a critical point of a functional $J\colon M\rightarrow
R$ where $M$ is a Riemannian manifold. Due to local quadratic convergence a …