35 citations · 62 across the 5 of their papers we have counts for
5 papers
Problem-Driven Scenario Reduction and Scenario Approximation for Robust Optimization
Jamie Fairbrother, Marc Goerigk, Mohammad Khosravi
In robust optimization, we would like to find a solution that is immunized against all scenarios that are modeled in an uncertainty set. Which scenarios to include in such a set is…
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language
Jamie Fairbrother, Christopher Nemeth, Maxime Rischard +2
Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Ga…
A Two-Level Graph Partitioning Problem Arising in Mobile Wireless Communications
Jamie Fairbrother, Adam Letchford, Keith Briggs
In the k-partition problem (k-PP), one is given an edge-weighted undirected graph, and one must partition the node set into at most k subsets, in order to minimise (or maximise) th…
Scenario generation for single-period portfolio selection problems with tail risk measures: coping with high dimensions and integer variables
Jamie Fairbrother, Amanda Turner, Stein Wallace
In this paper we propose a problem-driven scenario generation approach to the single-period portfolio selection problem which use tail risk measures such as conditional value-at-ri…
Problem-driven scenario generation: an analytical approach for stochastic programs with tail risk measure
Jamie Fairbrother, Amanda Turner, Stein Wallace
Scenario generation is the construction of a discrete random vector to represent parameters of uncertain values in a stochastic program. Most approaches to scenario generation are…