Optimization with gradient-boosted trees and risk control
File(s)
Working paper
Author(s)
Mistry, M
Letsios, D
Misener, R
Krennrich, G
Lee, RM
Type
Working Paper
Abstract
Decision trees effectively represent the sparse, high dimensional and noisy nature of chemical data from experiments. Having learned a function from this data, we may want to thereafter optimize the function, e.g., picking the best chemical process catalyst. In this way, we may repurpose legacy predictive models. This work studies a large-scale, industrially-relevant mixed-integer quadratic optimization problem involving: (i) gradient-boosted pre-trained regression trees modeling catalyst behavior, (ii) penalty functions mitigating risk, and (iii) penalties enforcing composition constraints. We develop heuristic methods and an exact, branch-and-bound algorithm leveraging structural properties of gradient-boosted trees and penalty functions. We numerically test our methods on an industrial instance.
Copyright Statement
© 2018 The Authors
Sponsor
BASF SE
Engineering and Physical Sciences Research Council
Identifier
http://arxiv.org/abs/1803.00952v1
Grant Number
85270950
EP/P016871/1
Subjects
math.OC
cs.AI
Notes
10 pages, 6 figures