1 code implementation • 17 Dec 2020 • Oliver Sheridan-Methven, Michael Giles
For numerical approximations to stochastic differential equations using the Euler-Maruyama scheme, we propose incorporating approximate random variables computed using low precisions, such as single and half precision.
Numerical Analysis Numerical Analysis 65G50, 65C10, 41A10, 65C05, 65Y20, 60H35, 65B10, 65L70, 34M30, 97N20, 65C30
2 code implementations • 17 Dec 2020 • Oliver Sheridan-Methven, Michael Giles
For random variables produced through the inverse transform method, approximate random variables are introduced, which are produced by approximations to a distribution's inverse cumulative distribution function.
Numerical Analysis Numerical Analysis 65C10, 41A10, 65D15, 65C05, 62E17, 65Y20, 60H35, 65C30
1 code implementation • 29 Dec 2018 • Coralia Cartis, Lindon Roberts, Oliver Sheridan-Methven
We apply a state-of-the-art, local derivative-free solver, Py-BOBYQA, to global optimization problems, and propose an algorithmic improvement that is beneficial in this context.
Optimization and Control
