Optimal control of acute myeloid leukaemia
Sharp, J
Browning, A
Mapder, T
Burrage, K
Simpson, M
A mathematical framework for the emergence of winners and losers in cell competition
Pak, T
Pitt-Francis, J
Baker, R
How domain growth is implemented determines the long term behaviour of a cell population through its effect on spatial correlations
Ross, R
Baker, R
Yates, C
The effect of domain growth on spatial correlations
Ross, R
Yates, C
Baker, R
Variable species densities are induced by volume exclusion interactions upon domain growth
Ross, R
Yates, C
Baker, R
Inference of cell-cell interactions from population density characteristics and cell trajectories on static and growing domains
Ross, R
Yates, C
Baker, R
Radiation resistant cancer cells enhance the survival and resistance of sensitive cells in prostate spheroids
Kannan, P
Paczkowski, M
Miar, A
Owen, J
Kretzschmar, W
Lucotti, S
Kaeppler, J
Chen, J
Markelc, B
Kunz-Schughart, L
Harris, A
Partridge, M
Byrne, H
Hensel lifting and bivariate polynomial factorisation over finite fields
Gao, S
Lauder, A
Mathematics of Computation
volume 71
issue 240
1663-1676
(05 Dec 2001)
Efferocytosis perpetuates substance accumulation inside macrophage populations
Ford, H
Zeboudj, L
Purvis, G
Bokum, A
Zarebski, A
Bull, J
Byrne, H
Myerscough, M
Greaves, D
Thu, 13 Jun 2024
14:00 -
15:00
Lecture Room 3
A New Two-Dimensional Model-Based Subspace Method for Large-Scale Unconstrained Derivative-Free Optimization: 2D-MoSub
Pengcheng Xie
(Chinese Academy of Sciences)
Abstract
This seminar will introduce 2D-MoSub, a derivative-free optimization method based on the subspace method and quadratic models, specifically tackling large-scale derivative-free problems. 2D-MoSub combines 2-dimensional quadratic interpolation models and trust-region techniques to update the points and explore the 2-dimensional subspace iteratively. Its framework includes constructing the interpolation set, building the quadratic interpolation model, performing trust-region trial steps, and updating the trust-region radius and subspace. Computation details and theoretical properties will be discussed. Numerical results demonstrate the advantage of 2D-MoSub.
Short Bio:
Pengcheng Xie, PhD (Chinese Academy of Sciences), is joining Lawrence Berkeley National Laboratory as a postdoctoral scholar specializing in mathematical optimization and numerical analysis. He has developed optimization methods, including 2D-MoSub and SUSD-TR. Pengcheng has published in major journals and presented at ISMP 2024 (upcoming), ICIAM 2023, and CSIAM 2022. He received the Hua Loo-keng scholarship in 2019 and the CAS-AMSS Presidential scholarship in 2023.