Myopic policies for budgeted optimization with constrained experiments Public Deposited

http://ir.library.oregonstate.edu/concern/defaults/4j03d454v

Graduation date: 2008

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  • Motivated by a real-world problem, we study a novel setting for budgeted optimization where the goal is to optimize an unknown function f(x) given a budget. In our setting, it is not practical to request samples of f(x) at precise input values due to the formidable cost of experimental setup at precise values. Rather, we may request constrained experiments, which give the experimenter constraints on x for which they must return f(x). Importantly, as the constraints become looser, the experimental cost decreases, but the uncertainty about the location of the next observation increases. Our problem is to manage this trade-off by selecting a sequence of constrained experiments to best optimize f within the budget. We propose a number of myopic policies for selecting constrained experiments using both model-free and model-based approaches, inspired by policies for unconstrained settings. Experiments on synthetic and real-world functions indicate that our policies outperform random selection, that the model-based policies are superior to model-free ones, and give insights into which policies are preferable overall.
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  • description.provenance : Made available in DSpace on 2009-01-20T16:20:42Z (GMT). No. of bitstreams: 1 report.pdf: 1122034 bytes, checksum: 03fabf0ff57fb3e643693f3fa18179d7 (MD5)
  • description.provenance : Submitted by Wei Lin (linw@onid.orst.edu) on 2009-01-16T23:47:53Z No. of bitstreams: 1 report.pdf: 1122034 bytes, checksum: 03fabf0ff57fb3e643693f3fa18179d7 (MD5)
  • description.provenance : Approved for entry into archive by Linda Kathman(linda.kathman@oregonstate.edu) on 2009-01-20T16:20:41Z (GMT) No. of bitstreams: 1 report.pdf: 1122034 bytes, checksum: 03fabf0ff57fb3e643693f3fa18179d7 (MD5)

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