Concept
Newton's method in optimization
Related publications (163)
Phase diagram of the J-Jd Heisenberg model on the maple leaf lattice: Neural networks and density matrix renormalization group
We microscopically analyze the nearest-neighbor Heisenberg model on the maple leaf lattice through neural quantum state (NQS) and infinite density matrix renormalization group (iDMRG) methods. Embarking to parameter regimes beyond the exact dimer singlet g ...
Amer Physical Soc2024Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations
Giancarlo Ferrari Trecate, Maryam Kamgarpour, Yuning Jiang, Baiwei Guo
We address black-box convex optimization problems, where the objective and constraint functions are not explicitly known but can be sampled within the feasible set. The challenge is thus to generate a sequence of feasible points converging towards an optim ...
2023Tensor approximation of the self-diffusion matrix of tagged particle processes
The objective of this paper is to investigate a new numerical method for the approximation of the self-diffusion matrix of a tagged particle process defined on a grid. While standard numerical methods make use of long-time averages of empirical means of de ...
ACADEMIC PRESS INC ELSEVIER SCIENCE2023Geometrically-Conditioned Point Diffusion Models
Pascal Fua, Eduard Trulls Fortuny, Michal Jan Tyszkiewicz
Diffusion models generating images conditionally on text, such as Dall-E 2 [51] and Stable Diffusion[53], have recently made a splash far beyond the computer vision com- munity. Here, we tackle the related problem of generating point clouds, both unconditi ...
2023GECCO: Geometrically-Conditioned Point Diffusion Models
Pascal Fua, Eduard Trulls Fortuny, Michal Jan Tyszkiewicz
Diffusion models generating images conditionally on text, such as Dall-E 2 [51] and Stable Diffusion[53], have recently made a splash far beyond the computer vision community. Here, we tackle the related problem of generating point clouds, both uncondition ...
Ieee Computer Soc2023Variational methods for finding periodic orbits in the incompressible Navier Stokes equations
Tobias Schneider, Jeremy Peter Parker
Unstable periodic orbits are believed to underpin the dynamics of turbulence, but by their nature are hard to find computationally. We present a family of methods to converge such unstable periodic orbits for the incompressible Navier-Stokes equations, bas ...
CAMBRIDGE UNIV PRESS2022Characterization and prediction of peptide structures on inorganic surfaces
Interfaces between peptides and metallic surfaces are the subject of great interest for possible use in technological and medicinal applications, mainly since organic systems present an extensive range of functionalities, are abundant, cheap, and exhibit l ...
EPFL2022On the Double Descent of Random Features Models Trained with SGD
We study generalization properties of random features (RF) regression in high dimensions optimized by stochastic gradient descent (SGD) in under-/overparameterized regime. In this work, we derive precise non-asymptotic error bounds of RF regression under b ...
2022Distributed Optimal Power Flow for VSC-MTDC Meshed AC/DC Grids Using ALADIN
Colin Neil Jones, Yuning Jiang, Xinliang Dai
The increasing application of voltage source converter (VSC) based high voltage direct current (VSC-HVDC) technology in power grids has raised the importance of incorporating DC grids and converters into the existing transmission network. This poses signif ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022Deficit Round-Robin: A Second Network Calculus Analysis
Jean-Yves Le Boudec, Seyed Mohammadhossein Tabatabaee
Deficit Round-Robin (DRR) is a widespread scheduling algorithm that provides fair queueing with variable-length packets. Bounds on worst-case delays for DRR were found by Boyer et al., who used a rigorous network calculus approach and characterized the ser ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022