Flexible characterization techniques that provide a detailed picture of the experimental
imperfections under realistic assumptions are crucial to gain actionable advice in the …

We develop a manifold inexact augmented Lagrangian framework to solve a family of
nonsmooth optimization problem on Riemannian submanifold embedding in Euclidean …

We propose a new globalization strategy of the damped Newton method for finding
singularities of a vector field on Riemannian manifolds. We establish its global convergence …

Finding a geodesic joining two given points in a complete path-connected Riemannian
manifold requires much more effort than determining a geodesic from initial data. This is …

We extend the classical primal-dual interior point method from the Euclidean setting to the
Riemannian one. Our method, named the Riemannian interior point method, is for solving …

We consider optimization problems on Riemannian manifolds with equality and inequality
constraints, which we call Riemannian nonlinear optimization (RNLO) problems. Although …

Trust-region methods have received massive attention in a variety of continuous
optimization. They aim to obtain a trial step by minimizing a quadratic model in a region of a …

In this paper, a new version of a secant-type method for solving constrained mixed
generalized equations is addressed. The method is a combination of the secant method …

We propose a higher-order method for solving nonsmooth optimization problems on
manifolds. To obtain superlinear convergence, we apply a Riemannian semismooth Newton …

In this paper, a deconvolution method is proposed to obtain the original nano-layer structure
directly from measured high-resolution depth profiling data with sputtering induced …