3 citations · 4 across the 3 of their papers we have counts for
3 papers
math.OC2022★ 3 cited
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…
math.OC2022★ 1 cited
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…
stat.AP2017
Gaussian Processes for Demand Unconstraining
Ilan Price, Jaroslav Fowkes, Daniel Hopman
One of the key challenges in revenue management is unconstraining demand data. Existing state of the art single-class unconstraining methods make restrictive assumptions about the…