Prof. Coralia Cartis
PhD Mathematics, University of Cambridge (2005)
Status
Academic Faculty
Professor of Numerical Optimization, Mathematical Institute
Tutorial Fellow in Mathematics, Balliol College
Turing Fellow, The Alan Turing Institute (London)
Research groups
Address
Mathematical Institute
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG
Major / recent publications
- C. Cartis and A. Thompson
An exact tree projection algorithm for wavelets
ERGO Technical Report 13-006, School of Mathematics, Edinburgh University, 2013.
To appear in IEEE Signal Processing Letters (2013) - S. F. B. Tett, M. J. Mineter, C. Cartis, D. J. Rowlands and P. Liu
Can top of atmosphere radiation measurements constrain climate predictions? Part 1: Tuning
To appear in Journal of Climate (2013) - S. F. B. Tett, D. J. Rowlands, M. J. Mineter and C. Cartis
Can top of atmosphere radiation measurements constrain climate predictions? Part 2: Climate Sensitivity
To appear in Journal of Climate (2013) - C. Cartis, N. I. M. Gould and Ph. L. Toint
On the evaluation complexity of cubic regularization methods for potentially rank-deficient nonlinear
least-squares problems and its relevance to constrained nonlinear optimization
SIAM Journal on Optimization, vol. 23(3):1553--1574, 2013. (doi: 10.1137/120869687) - C. Cartis, N. I. M. Gould and Ph. L. Toint
How much patience do you have? A worst-case perspective on smooth nonconvex optimization
OPTIMA 88, 2012. (feature article of the Mathematical Optimization Society Newsletter) - C. Cartis, N. I. M. Gould and Ph. L. Toint.
On the complexity of finding first-order critical points in constrained nonlinear programming.
Mathematical Programming Series A, (DOI) 10.1007/s10107-012-0617-9 (Online First, 2012) - C. Cartis, N. I. M. Gould and Ph. L. Toint
Adaptive cubic regularisation methods for unconstrained optimization. Part II: worst-case function- and derivative-evaluation complexity
Mathematical Programming, vol. 130(2), pp. 295--319, 2011. - C. Cartis, N. I. M. Gould and Ph. L. Toint
Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results
Mathematical Programming, vol. 127(2), pp. 245--295, 2011. - J. D. Blanchard, C. Cartis, J. Tanner and A. Thompson
Phase transitions for greedy sparse approximation algorithms
Applied and Computational Harmonic Analysis, vol. 30(2), pp. 188--203, 2011. - J. D. Blanchard, C. Cartis and J. Tanner
Compressed sensing: how sharp is the restricted isometry property?
SIAM Review, vol. 53(1), pp. 105--125, 2011.
Prizes, awards, and scholarships
Leslie Fox Prize in Numerical Analysis 2005 (second place)
2019 Best paper prize of the Mathematical Programming Computation Journal (joint)
INFORMS Simulation Society Outstanding Paper Prize, 2021 (joint)
EUROPT Fellow 2023
SIAM Fellow 2023
Recent publications
Cubic-quartic regularization models for solving polynomial subproblems in third-order tensor methods
Cartis, C Zhu, W Mathematical Programming 1-53 (06 Jan 2025) Using Shortened Spin‐Ups to Speed Up Ocean Biogeochemical Model Optimization
Oliver, S Khatiwala, S Cartis, C Ward, B Kriest, I Journal of Advances in Modeling Earth Systems volume 16 issue 9 (10 Sep 2024) Convergent least-squares optimization methods for variational data assimilation
Cartis, C Kaouri, M Lawless, A Nichols, N Optimization volume ahead-of-print issue ahead-of-print 1-35 (19 Aug 2024) Registration of algebraic varieties using Riemannian optimization.
Goyens, F Cartis, C Chrétien, S CoRR volume abs/2401.08562 (2024) Corrigendum to “Nonlinear matrix recovery using optimization on the Grassmann manifold” [Appl. Comput. Harmon. Anal. 62 (2023) 498–542]
Goyens, F Cartis, C Eftekhari, A Applied and Computational Harmonic Analysis volume 63 93 (01 Mar 2023) Research interests
Algorithm design, analysis and implementation for linear and nonlinear optimization, convex and nonconvex problems, large-scale
- Complexity of optimization problems and algorithms
- Interconnections between dynamical systems and continuous optimization
- Applications: compressed sensing and sparse approximation, machine learning
- Applications: inverse problems in climate modelling