536 citations · 1.1k across the 24 of their papers we have counts for
26 papers
Ultrafast Nonthermal Lattice Destabilization and Suppression of Polar Optical Scattering in Electronically Excited -SiO from First-Principles and Deep Neural Network Potential Modeling
Iyyappa Rajan Panneerselvam, Mark Yeung, Charlotte Palmer +2
We present a multiscale first-principles-to-machine-learning approach to investigate ultrafast lattice dynamics in electronically excited -SiO. Ab initio molecular dynamics…
Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient
Puya Latafat, Andreas Themelis, Lorenzo Stella +1
Backtracking linesearch is the de facto approach for minimizing continuously differentiable functions with locally Lipschitz gradient. In recent years, it has been shown that in th…
Thermoelectric properties of cement composite analogues from first principles calculations
Esther Orisakwe, Conrad Johnston, Ruchita Jani +8
Buildings are responsible for a considerable fraction of the energy wasted globally every year, and as a result, excess carbon emissions. While heat is lost directly in colder mont…
Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type linesearch algorithms
Andreas Themelis, Lorenzo Stella, Panagiotis Patrinos
Although the performance of popular optimization algorithms such as Douglas-Rachford splitting (DRS) and the ADMM is satisfactory in small and well-scaled problems, ill conditionin…
Newton-type Alternating Minimization Algorithm for Convex Optimization
Lorenzo Stella, Andreas Themelis, Panagiotis Patrinos
We propose NAMA (Newton-type Alternating Minimization Algorithm) for solving structured nonsmooth convex optimization problems where the sum of two functions is to be minimized, on…
Proximal Gradient Algorithms: Applications in Signal Processing
Niccolò Antonello, Lorenzo Stella, Panagiotis Patrinos +1
Advances in numerical optimization have supported breakthroughs in several areas of signal processing. This paper focuses on the recent enhanced variants of the proximal gradient n…