We provide an explicit formula for the Levi-Civita connection and Riemannian Hessian for a Riemannian manifold that is a quotient of a manifold embedded in an inner product space with a non-constant metric function. Together with a classical formula for projection, this allows us to evaluate Riemannian gradient and Hessian for several families of metrics on classical manifolds, including a family of metrics on Stiefel manifolds connecting both the constant and canonical ambient metrics with closed-form geodesics. Using these formulas, we derive Riemannian optimization frameworks on quotients of Stiefel manifolds, including flag manifolds, and a new family of complete quotient metrics on the manifold of positive-semidefinite matrices of fixe...
Abstract—The squared distance function is one of the standard functions on which an optimization alg...
A preliminary version appeared as INRIA Research Report 5255, July 2004.Tensors are nowadays a commo...
International audienceWe describe four algorithms for neural network training, each adapted to diffe...
We provide two closed-form geodesic formulas for a family of metrics on Stiefel manifold, parameteri...
Optimization problems on the generalized Stiefel manifold (and products of it) are prevalent across ...
We consider smooth optimization problems with a Hermitian positive semi-definite fixed-rank constrai...
Several applications in optimization, image, and signal processing deal with data belonging to matri...
We give simple formulas for the canonical metric, gradient, Lie derivative, Riemannian connection, p...
Manifold optimization appears in a wide variety of computational problems in the applied sciences. I...
In this paper, we present a novel penalty model called ExPen for optimization over the Stiefel manif...
How to make the best decision? This general concern, pervasive in both research and industry, is wha...
Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank...
International audienceSymmetric positive definite (SPD) matrices are geometric data that appear in m...
Let $*$ denote the t-product between two third-order tensors. The purpose of this work is to study f...
AbstractThe Riemannian metric on the manifold of positive definite matrices is defined by a kernel f...
Abstract—The squared distance function is one of the standard functions on which an optimization alg...
A preliminary version appeared as INRIA Research Report 5255, July 2004.Tensors are nowadays a commo...
International audienceWe describe four algorithms for neural network training, each adapted to diffe...
We provide two closed-form geodesic formulas for a family of metrics on Stiefel manifold, parameteri...
Optimization problems on the generalized Stiefel manifold (and products of it) are prevalent across ...
We consider smooth optimization problems with a Hermitian positive semi-definite fixed-rank constrai...
Several applications in optimization, image, and signal processing deal with data belonging to matri...
We give simple formulas for the canonical metric, gradient, Lie derivative, Riemannian connection, p...
Manifold optimization appears in a wide variety of computational problems in the applied sciences. I...
In this paper, we present a novel penalty model called ExPen for optimization over the Stiefel manif...
How to make the best decision? This general concern, pervasive in both research and industry, is wha...
Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank...
International audienceSymmetric positive definite (SPD) matrices are geometric data that appear in m...
Let $*$ denote the t-product between two third-order tensors. The purpose of this work is to study f...
AbstractThe Riemannian metric on the manifold of positive definite matrices is defined by a kernel f...
Abstract—The squared distance function is one of the standard functions on which an optimization alg...
A preliminary version appeared as INRIA Research Report 5255, July 2004.Tensors are nowadays a commo...
International audienceWe describe four algorithms for neural network training, each adapted to diffe...