arXiv subjects

Caio O. da Silva

Publications and source records attributed to Caio O. da Silva.

3 recordsLinked to original sources

arXiv2025

A thorough study of Riemannian Newton's Method

This work presents a thorough numerical study of Riemannian Newton's Method (RNM) for optimization problems, with a focus on the Grassmannian and on the Stiefel manifold. We compare the Riemannian formulation of Newton's Method with its classical Euclidean counterpart based on Lagrange multipliers by applying both approaches to the important and challenging Hartree--Fock energy minimization problem from Quantum Chemistry. Experiments on a dataset of 125 molecules show that the Riemannian approaches achieve higher convergence rates, require fewer iterations, and exhibit greater robustness to the choice of initial guess. In this work we also analyze the numerical issues that arise from using Newton's Method on the total manifold when the cost function is defined on the quotient manifold. We investigate the performance of a modified RNM in which we ignore the small eigenvalues of the Hessian and the results indicate that this modified method is stable and performs on par with the RNM on the quotient manifold.

math.OC↗

arXiv2024

Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion Estimation

Single camera 3D pose estimation is an ill-defined problem due to inherent ambiguities from depth, occlusion or keypoint noise. Multi-hypothesis pose estimation accounts for this uncertainty by providing multiple 3D poses consistent with the 2D measurements. Current research has predominantly concentrated on generating multiple hypotheses for single frame static pose estimation or single hypothesis motion estimation. In this study we focus on the new task of multi-hypothesis motion estimation. Multi-hypothesis motion estimation is not simply multi-hypothesis pose estimation applied to multiple frames, which would ignore temporal correlation across frames. Instead, it requires distributions which are capable of generating temporally consistent samples, which is significantly more challenging than multi-hypothesis pose estimation or single-hypothesis motion estimation. To this end, we introduce Platypose, a framework that uses a diffusion model pretrained on 3D human motion sequences for zero-shot 3D pose sequence estimation. Platypose outperforms baseline methods on multiple hypotheses for motion estimation. Additionally, Platypose also achieves state-of-the-art calibration and competitive joint error when tested on static poses from Human3.6M, MPI-INF-3DHP and 3DPW. Finally, because it is zero-shot, our method generalizes flexibly to different settings such as multi-camera inference.

cs.CV↗

arXiv2024

Riemannian Optimization and the Hartree-Fock Method

In the present work we studied a subfield of Applied Mathematics called Riemannian Optimization. The main goal of this subfield is to generalize algorithms, theorems and tools from Mathematical Optimization to the case in which the optimization problem is defined on a Riemannian manifold. As a case study, we implemented some of the main algorithms described in the literature (Gradient Descent, Newton-Raphson and Conjugate Gradient) to solve an optimization problem known as Hartree-Fock. This method is extremely important in the field of Computational Quantum Chemistry and it is a good case study because it is a problem somewhat hard to solve and, as a consequence of this, it requires many tools from Riemannian Optimization. Besides, it is also a good example to see how these algorithms perform in practice.

math.OC↗