Mathematical optimization is an important branch of applied mathematics. Different classes
of optimization problems are categorized based on their problem structures. While there are …

In this paper, we consider the composite optimization problems over the Stiefel manifold. A
successful method to solve this class of problems is the proximal gradient method proposed …

We propose a class of multipliers correction methods to minimize a differentiable function
over the Stiefel manifold. The proposed methods combine a function value reduction step …

We present a reformulation of optimization problems over the Stiefel manifold by using a
Cayley-type transform, named the generalized left-localized Cayley transform, for the Stiefel …

We present an adaptive parametrization strategy for optimization problems over the Stiefel
manifold by using generalized Cayley transforms to utilize powerful Euclidean optimization …

Optimization problems with orthogonality constraints appear widely in applications from
science and engineering. We address these types of problems from a numerical approach …

In this paper we consider a class of iterative gradient projection methods for solving
optimization problems with orthogonality constraints. The proposed method can be seen as …

This paper proposes a Newton-type method to solve numerically the eigenproblem of
several diagonalizable matrices, which pairwise commute. A classical result states that …

The Adaptive Localized Cayley Parametrization (ALCP) strategy for orthogonality
constrained optimization has been proposed as a scheme to utilize Euclidean optimization …

Optimization under the symplecticity constraint is an approach for solving various problems
in quantum physics and scientific computing. Building on the results that this optimization …