نتایج جستجو برای: hessian manifolds

تعداد نتایج: 34606  

2012
Keiko Uohashi

In this paper we study harmonic maps relative to α-connections, and not always relative to Levi-Civita connections, on statistical manifolds. In particular, harmonic maps on α-conformally equivalent statistical manifolds are discussed, and conditions for harmonicity are given by parameters α and dimensions n. As the application we also describe harmonic maps between level surfaces of a Hessian ...

Journal: :Indiana University Mathematics Journal 2015

Journal: :Proceedings of the American Mathematical Society 2022

In this paper, we consider a class of Hessian type equations which include the $(n-1)$ Monge-Amp\`{e}re equation on Riemannian manifolds. The \emph{a priori} $C^2$ estimates and existence solutions are established.

Journal: :Journal of Functional Analysis 2021

In this paper, we derive an a priori second order estimate for solutions which are in Γk+1 cone to class of complex Hessian equations with both sides the equation depending on gradient compact Hermitian manifolds.

Journal: :Calculus of Variations and Partial Differential Equations 2023

In this paper, we establish a priori estimates for solutions of general class fully non-linear equations on compact almost Hermitian manifolds. As an application, solve the complex Hessian equation and Monge–Ampère $$(n-1)$$ -plurisubharmonic functions in setting.

Journal: :Discrete and Continuous Dynamical Systems 2021

We derive second order estimates for \begin{document}$ \chi $\end{document}-plurisubharmonic solutions of complex Hessian equations with right hand side depending on the gradient compact Hermitian manifolds.

2015
Nicolas Boumal

The Riemannian trust-region algorithm (RTR) is designed to optimize differentiable cost functions on Riemannian manifolds. It proceeds by iteratively optimizing local models of the cost function. When these models are exact up to second order, RTR boasts a quadratic convergence rate to critical points. In practice, building such models requires computing the Riemannian Hessian, which may be cha...

Journal: :Journal of Machine Learning Research 2008
Yair Goldberg Alon Zakai Dan Kushnir Yaacov Ritov

We analyze the performance of a class of manifold-learning algorithms that find their output by minimizing a quadratic form under some normalization constraints. This class consists of Locally Linear Embedding (LLE), Laplacian Eigenmap, Local Tangent Space Alignment (LTSA), Hessian Eigenmaps (HLLE), and Diffusion maps. We present and prove conditions on the manifold that are necessary for the s...

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