Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Konstantinos C. Zygalakis"'
Autor:
Styliani Kontogeorgaki, Rubén J. Sánchez-García, Rob M. Ewing, Konstantinos C. Zygalakis, Ben D. MacArthur
Publikováno v:
Scientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
Abstract Signaling networks mediate environmental information to the cell nucleus. To perform this task effectively they must be able to integrate multiple stimuli and distinguish persistent signals from transient environmental fluctuations. However,
Externí odkaz:
https://doaj.org/article/1575396668054f3e924a7fe85fe7ea65
Publikováno v:
Statistics and Computing. 33
In this paper, we analyse a proximal method based on the idea of forward–backward splitting for sampling from distributions with densities that are not necessarily smooth. In particular, we study the non-asymptotic properties of the Euler–Maruyam
Publikováno v:
SIAM Journal on Imaging Sciences. 15:892-924
Publikováno v:
Sanz-Serna, J M & Zygalakis, K C 2021, ' The connections between Lyapunov functions for some optimization algorithms and differential equations ', Siam journal on numerical analysis, vol. 59, no. 3, pp. 1542-1565 . https://doi.org/10.1137/20M1364138
In this manuscript, we study the properties of a family of second-order differential equations with damping, its discretizations and their connections with accelerated optimization algorithms for $m$-strongly convex and $L$-smooth functions. In parti
Publikováno v:
Vargas Mieles, L, Pereyra, M & Zygalakis, K C 2020, ' Accelerating proximal Markov chain Monte Carlo by using an explicit stabilised method ', Siam journal on imaging sciences, vol. 13, no. 2 . https://doi.org/10.1137/19M1283719
We present a highly efficient proximal Markov chain Monte Carlo methodology to perform Bayesian computation in imaging problems. Similarly to previous proximal Monte Carlo approaches, the proposed method is derived from an approximation of the Langev
Publikováno v:
Royal Society Open Science
Royal Society Open Science, Vol 8, Iss 10 (2021)
Royal Society Open Science, Vol 8, Iss 10 (2021)
We consider spectral methods that uncover hidden structures in directed networks. We establish and exploit connections between node reordering via (a) minimizing an objective function and (b) maximizing the likelihood of a random graph model. We focu
Triadic closure describes the tendency for new friendships to form between individuals who already have friends in common. It has been argued heuristically that the triadic closure effect can lead to bistability in the formation of large-scale social
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f73543a2e32f3b44fecc5de652ef203c
Publikováno v:
Sanz-Serna, J M & Zygalakis, K C 2020, ' Contractivity of Runge-Kutta methods for convex gradient systems ', Siam journal on numerical analysis, vol. 58, no. 4, pp. 2079–2092 . https://doi.org/10.1137/19M1299256
We consider the application of Runge-Kutta (RK) methods to gradient systems $(d/dt)x = -\nabla V(x)$, where, as in many optimization problems, $V$ is convex and $\nabla V$ (globally) Lipschitz-continuous with Lipschitz constant $L$. Solutions of this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d06f61cbd5054c906a03f822a52d6c6
https://www.pure.ed.ac.uk/ws/files/151364097/1909.09971.pdf
https://www.pure.ed.ac.uk/ws/files/151364097/1909.09971.pdf
Autor:
Armand Jordana, Cameron A. Smith, Christian A. Yates, Konstantinos C. Zygalakis, Adam George, Andrew B. Duncan
Publikováno v:
Yates, K, George, A, Jordana, A, Smith, C, Duncan, A & Zygalakis, K 2020, ' The blending region hybrid framework for the simulation of stochastic reaction-diffusion processes ', Journal of the Royal Society, Interface, vol. 17, no. 171 . https://doi.org/10.1098/rsif.2020.0563
Journal of the Royal Society Interface
Yates, C A, George, A, Jordana, A, Smith, C A, Duncan, A B & Zygalakis, K C 2020, ' The blending region hybrid framework for the simulation of stochastic reaction-diffusion processes ', Journal of the Royal Society, Interface, vol. 17, no. 171, 20200563 . https://doi.org/10.1098/rsif.2020.0563
Journal of the Royal Society Interface
Yates, C A, George, A, Jordana, A, Smith, C A, Duncan, A B & Zygalakis, K C 2020, ' The blending region hybrid framework for the simulation of stochastic reaction-diffusion processes ', Journal of the Royal Society, Interface, vol. 17, no. 171, 20200563 . https://doi.org/10.1098/rsif.2020.0563
The simulation of stochastic reaction-diffusion systems using fine-grained representations can become computationally prohibitive when particle numbers become large. If particle numbers are sufficiently high then it may be possible to ignore stochast
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7cd6d4892145241ec0aabef674d87b2
Autor:
Rob M. Ewing, Ben D. MacArthur, Styliani Kontogeorgaki, Konstantinos C. Zygalakis, Rubén J. Sánchez-García
Publikováno v:
Scientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
Kontogeorgaki, S, Sánchez-García, R J, Ewing, R M, Zygalakis, K C & MacArthur, B D 2017, ' Noise-processing by signaling networks ', Scientific Reports, vol. 7, no. 1, 532, pp. 532 . https://doi.org/10.1038/s41598-017-00659-x
Scientific Reports
Kontogeorgaki, S, Sánchez-García, R J, Ewing, R M, Zygalakis, K C & MacArthur, B D 2017, ' Noise-processing by signaling networks ', Scientific Reports, vol. 7, no. 1, 532, pp. 532 . https://doi.org/10.1038/s41598-017-00659-x
Scientific Reports
Signaling networks mediate environmental information to the cell nucleus. To perform this task effectively they must be able to integrate multiple stimuli and distinguish persistent signals from transient environmental fluctuations. However, the ways