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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…