4 citations · 20 across the 10 of their papers we have counts for
17 papers · 1 filter
Random Subspace Cubic-Regularization Methods, with Applications to Low-Rank Functions
Coralia Cartis, Zhen Shao, Edward Tansley
We propose and analyze random subspace variants of the second-order Adaptive Regularization using Cubics (ARC) algorithm. These methods iteratively restrict the search space to som…
Scalable Second-Order Optimization Algorithms for Minimizing Low-rank Functions
Edward Tansley, Coralia Cartis
We present a random-subspace variant of cubic regularization algorithm that chooses the size of the subspace adaptively, based on the rank of the projected second derivative matrix…
Randomised subspace methods for non-convex optimization, with applications to nonlinear least-squares
Coralia Cartis, Jaroslav Fowkes, Zhen Shao
We propose a general random subspace framework for unconstrained nonconvex optimization problems that requires a weak probabilistic assumption on the subspace gradient, which we sh…
A Randomised Subspace Gauss-Newton Method for Nonlinear Least-Squares
Coralia Cartis, Jaroslav Fowkes, Zhen Shao
We propose a Randomised Subspace Gauss-Newton (R-SGN) algorithm for solving nonlinear least-squares optimization problems, that uses a sketched Jacobian of the residual in the vari…
Convergent least-squares optimisation methods for variational data assimilation
Coralia Cartis, Maha H. Kaouri, Amos S. Lawless +1
Data assimilation combines prior (or background) information with observations to estimate the initial state of a dynamical system over a given time-window. A common application is…
Global optimization using random embeddings
Coralia Cartis, Estelle Massart, Adilet Otemissov
We propose a random-subspace algorithmic framework for global optimization of Lipschitz-continuous objectives, and analyse its convergence using novel tools from conic integral geo…