196 citations · 537 across the 39 of their papers we have counts for
45 papers
Optimal scaling of MCMC algorithms: the Hamiltonian approach
P. Dobson, J. M. Sanz-Serna, K. C. Zygalakis
We present a simple, yet general approach to study the scaling properties as the dimensionality of Metropolised MCMC sampling algorithms increases. The study relies on the symmetri…
Explicit stabilized implementation of singly diagonally implicit Runge-Kutta methods
Ibrahim Almuslimani, Gilles Vilmart, Konstantinos Zygalakis
Implicit methods are a natural approach for the integration of stiff differential equations, to avoid time-step restrictions faced by standard explicit integrators. Explicit stabil…
Accelerated optimization algorithms and ordinary differential equations: the convex non Euclidean case
Paul Dobson, Jesus María Sanz-Serna, Konstantinos C. Zygalakis
We study the connections between ordinary differential equations and optimization algorithms in a non-Euclidean setting. We propose a novel accelerated algorithm for minimising con…
Statistical modelling and Bayesian inversion for a Compton imaging system: application to radioactive source localisation
Cecilia Tarpau, Ming Fang, Konstantinos C. Zygalakis +3
This paper presents a statistical forward model for a Compton imaging system, called Compton imager. This system, under development at the University of Illinois Urbana Champaign,…
A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation
Thomas Trigo Trindade, Konstantinos C. Zygalakis
We consider the problem of efficiently simulating stochastic models of chemical kinetics. The Gillespie Stochastic Simulation algorithm (SSA) is often used to simulate these models…
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…