Paper

Stochastic quasi-Newton with adaptive step lengths for large-scale problems

We provide a numerically robust and fast method capable of exploiting the local geometry when solving large-scale stochastic optimisation problems. Our key innovation is an auxiliary variable construction coupled with an inverse Hessian approximation computed using a receding history of iterates and gradients... (read more)

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