196 citations · 537 across the 39 of their papers we have counts for
5 papers · 1 filter
Backward error analysis and the qualitative behaviour of stochastic optimization algorithms: Application to stochastic coordinate descent
Stefano Di Giovacchino, Desmond J. Higham, Konstantinos Zygalakis
Stochastic optimization methods have been hugely successful in making large-scale optimization problems feasible when computing the full gradient is computationally prohibitive. Us…
Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling
Teresa Klatzer, Paul Dobson, Yoann Altmann +3
This paper presents a new accelerated proximal Markov chain Monte Carlo methodology to perform Bayesian inference in imaging inverse problems with an underlying convex geometry. Th…
Gaussian processes for Bayesian inverse problems associated with linear partial differential equations
Tianming Bai, Aretha L. Teckentrup, Konstantinos C. Zygalakis
This work is concerned with the use of Gaussian surrogate models for Bayesian inverse problems associated with linear partial differential equations. A particular focus is on the r…
On the connections between optimization algorithms, Lyapunov functions, and differential equations: theory and insights
Paul Dobson, Jesus Maria Sanz-Serna, Konstantinos Zygalakis
We revisit the general framework introduced by Fazylab et al. (SIAM J. Optim. 28, 2018) to construct Lyapunov functions for optimization algorithms in discrete and continuous time.…
Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures
Tapio Helin, Andrew Stuart, Aretha Teckentrup +1
Bayesian posterior distributions arising in modern applications, including inverse problems in partial differential equation models in tomography and subsurface flow, are often com…