Manifold parameterizations have been applied to various fields of commercial industries. Several efficient algorithms for the computation of triangular surface mesh parameterizations have been proposed in recent years. However, the computation of tetrahedral volumetric mesh parameterizations is more challenging due to the fact that the number of mesh points would become enormously large when the higher-resolution mesh is considered and the bijectivity of parameterizations is more difficult to guarantee. In this paper, we develop a novel volumetric stretch energy minimization algorithm for volume-preserving parameterizations of simply connected 3-manifolds with a single boundary under the restriction that the boundary is a spherical area-preserving mapping. In addition, our algorithm can also be applied to compute spherical angle- and area-preserving parameterizations of genus-zero closed surfaces, respectively. Several numerical experiments indicate that the developed algorithms are more efficient and reliable compared to other existing algorithms. Numerical results on applications of the manifold partition and the mesh processing for three-dimensional printing are demonstrated thereafter to show the robustness of the proposed algorithm.
Li-Ping ZhangDepartment of Mathematical Sciences, Tsinghua UniversityLiqun QiDepartment of Applied Mathematics, Hong Kong Polytechnic UniversityGuanglu ZhouDepartment of Mathematics and Statistics, Curtin University, Perth, Australia
Numerical Linear Algebramathscidoc:1804.26001
SIAM Journal on Matrix Analysis and Applications, 35, (2), 437-452, 2014.4
We introduceM-tensors. This concept extends the concept ofM-matrices. We denote
Z-tensors as the tensors with nonpositive off-diagonal entries. We show that M-tensors must be Ztensors
and the maximal diagonal entry must be nonnegative. The diagonal elements of a symmetric
M-tensor must be nonnegative. A symmetric M-tensor is copositive. Based on the spectral theory
of nonnegative tensors, we show that the minimal value of the real parts of all eigenvalues of an Mtensor
is its smallest H+-eigenvalue and also is its smallest H-eigenvalue. We show that a Z-tensor is
an M-tensor if and only if all its H+-eigenvalues are nonnegative. Some further spectral properties of
M-tensors are given. We also introduce strong M-tensors, and some corresponding conclusions are
given. In particular, we show that all H-eigenvalues of strong M-tensors are positive. We apply this
property to study the positive definiteness of a class of multivariate forms associated with Z-tensors.
We also propose an algorithm for testing the positive definiteness of such a multivariate form.
In this paper, we focus on the stochastic inverse eigenvalue problem of reconstructing a stochastic matrix from the prescribed spectrum. We directly reformulate the stochastic inverse eigenvalue problem as a constrained optimization problem over several matrix manifolds to minimize the distance between isospectral matrices and stochastic matrices. Then we propose a geometric Polak–Ribi`ere–Polyak-based nonlinear conjugate gradient method for solving the constrained optimization problem. The global convergence of the proposed method is established. Our method can also be extended to the stochastic inverse eigenvalue problem with prescribed entries. An extra advantage is that our models yield new isospectral flow methods. Finally, we report some numerical tests to illustrate the efficiency of the proposed method for solving the stochastic inverse eigenvalue problem and the case of prescribed entries.