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1. Variational Observer Designs on Lie Groups, with Applications to Rigid Body Motion Estimation

   https://doi.org/10.1109/CDC56724.2024.10886160  

   Srinivasu, Neon; Dongare, Abhijit; Sanyal, Amit K (December 2024, IEEE)

   Variational estimation of a mechanical system is based on the application of variational principles from mechanics to state estimation of the system evolving on its configuration manifold. If the configuration manifold is a Lie group, then the underlying group structure can be used to design nonlinearly stable observers for estimation of configuration and velocity states from measurements. Measured quantities are on a vector space on which the Lie group acts smoothly. We formulate the design of variational observers on a general finite-dimensional Lie group, followed by the design and experimental evaluation of a variational observer for rigid body motions on the Lie group SE(3). 

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2. Accelerated Optimization on Riemannian Manifolds via Discrete Constrained Variational Integrators

   https://doi.org/10.1007/s00332-022-09795-9  

   Duruisseaux, Valentin; Leok, Melvin (April 2022, Journal of Nonlinear Science)

   Abstract A variational formulation for accelerated optimization on normed vector spaces was recently introduced in Wibisono et al. (PNAS 113:E7351–E7358, 2016), and later generalized to the Riemannian manifold setting in Duruisseaux and Leok (SJMDS, 2022a). This variational framework was exploited on normed vector spaces in Duruisseaux et al. (SJSC 43:A2949–A2980, 2021) using time-adaptive geometric integrators to design efficient explicit algorithms for symplectic accelerated optimization, and it was observed that geometric discretizations which respect the time-rescaling invariance and symplecticity of the Lagrangian and Hamiltonian flows were substantially less prone to stability issues, and were therefore more robust, reliable, and computationally efficient. As such, it is natural to develop time-adaptive Hamiltonian variational integrators for accelerated optimization on Riemannian manifolds. In this paper, we consider the case of Riemannian manifolds embedded in a Euclidean space that can be characterized as the level set of a submersion. We will explore how holonomic constraints can be incorporated in discrete variational integrators to constrain the numerical discretization of the Riemannian Hamiltonian system to the Riemannian manifold, and we will test the performance of the resulting algorithms by solving eigenvalue and Procrustes problems formulated as optimization problems on the unit sphere and Stiefel manifold. 

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3. Unit sphere fibrations in Euclidean space

   https://doi.org/10.1017/S0013091524000038  

   Asimov, Daniel; Frick, Florian; Harrison, Michael; Pegden, Wesley (May 2024, Proceedings of the Edinburgh Mathematical Society)

   We show that if an open set in $$\mathbb R^d$$ can be fibered by unit $$n$$-spheres, then $$d\le 2n+1$$, and if $d=2n+1$, then the spheres must be pairwise linked, and $$n\in \{0,1,3,7\}$$. For these values of $$n$$, we construct unit $$n$$-sphere fibrations in $$R^{2n+1}$$. 

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4. Four-manifolds of pinched sectional curvature

   https://doi.org/10.1016/j.aim.2018.07.003  

   Cao, Xiaodong; Tran, Hung (January 2022, Pacific journal of mathematics)

   In this paper, we study closed four-dimensional manifolds. In particular, we show that under various pinching curvature conditions (for example, the sectional curvature is no more than 5 6 of the smallest Ricci eigenvalue), the manifold is definite. If restricting to a metric with harmonic Weyl tensor, then it must be self-dual or anti-self-dual under the same conditions. Similarly, if restricting to an Einstein metric, then it must be either the complex projective space with its Fubini-Study metric, the round sphere, or their quotients. Furthermore, we also classify Einstein manifolds with positive intersection form and an upper bound on the sectional curvature. 

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5. Extrinsic Gaussian Processes for Regression and Classification on Manifolds

   https://doi.org/10.1214/18-BA1135  

   Lin, Lizhen; Mu, Niu; Cheung, Pokman; Dunson, David (January 2018, Bayesian Analysis)

   Gaussian processes (GPs) are very widely used for modeling of unknown functions or surfaces in applications ranging from regression to classification to spatial processes. Although there is an increasingly vast literature on applications, methods, theory and algorithms related to GPs, the overwhelming majority of this literature focuses on the case in which the input domain corresponds to a Euclidean space. However, particularly in recent years with the increasing collection of complex data, it is commonly the case that the input domain does not have such a simple form. For example, it is common for the inputs to be restricted to a non-Euclidean manifold, a case which forms the motivation for this article. In particular, we propose a general extrinsic framework for GP modeling on manifolds, which relies on embedding of the manifold into a Euclidean space and then constructing extrinsic kernels for GPs on their images. These extrinsic Gaussian processes (eGPs) are used as prior distributions for unknown functions in Bayesian inferences. Our approach is simple and general, and we show that the eGPs inherit fine theoretical properties from GP models in Euclidean spaces. We consider applications of our models to regression and classification problems with predictors lying in a large class of manifolds, including spheres, planar shape spaces, a space of positive definite matrices, and Grassmannians. Our models can be readily used by practitioners in biological sciences for various regression and classification problems, such as disease diagnosis or detection. Our work is also likely to have impact in spatial statistics when spatial locations are on the sphere or other geometric spaces. 

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