Optimization over Continuous and Multi-dimensional Decisions with Observational Data
© 2018 Curran Associates Inc.All rights reserved. We consider the optimization of an uncertain objective over continuous and multidimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable, asymptotically...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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2022
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| Online Access: | https://hdl.handle.net/1721.1/137378.2 |
| _version_ | 1810976339991199744 |
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| author | Bertsimas, Dimitris J McCord, Christopher |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Bertsimas, Dimitris J McCord, Christopher |
| author_sort | Bertsimas, Dimitris J |
| collection | MIT |
| description | © 2018 Curran Associates Inc.All rights reserved. We consider the optimization of an uncertain objective over continuous and multidimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable, asymptotically consistent, and superior to comparable methods on example problems. Our approach leverages predictive machine learning methods and incorporates information on the uncertainty of the predicted outcomes for the purpose of prescribing decisions. We demonstrate the efficacy of our method on examples involving both synthetic and real data sets. |
| first_indexed | 2024-09-23T08:53:43Z |
| format | Article |
| id | mit-1721.1/137378.2 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:53:43Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | mit-1721.1/137378.22022-01-07T15:48:55Z Optimization over Continuous and Multi-dimensional Decisions with Observational Data Bertsimas, Dimitris J McCord, Christopher Sloan School of Management Massachusetts Institute of Technology. Operations Research Center © 2018 Curran Associates Inc.All rights reserved. We consider the optimization of an uncertain objective over continuous and multidimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable, asymptotically consistent, and superior to comparable methods on example problems. Our approach leverages predictive machine learning methods and incorporates information on the uncertainty of the predicted outcomes for the purpose of prescribing decisions. We demonstrate the efficacy of our method on examples involving both synthetic and real data sets. 2022-01-07T15:48:54Z 2021-11-04T17:16:47Z 2022-01-07T15:48:54Z 2018-12 2021-02-04T18:39:50Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137378.2 Bertsimas, D and McCord, C. 2018. "Optimization over Continuous and Multi-dimensional Decisions with Observational Data." Advances in Neural Information Processing Systems, 2018-December. en Advances in Neural Information Processing Systems Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/octet-stream Neural Information Processing Systems (NIPS) |
| spellingShingle | Bertsimas, Dimitris J McCord, Christopher Optimization over Continuous and Multi-dimensional Decisions with Observational Data |
| title | Optimization over Continuous and Multi-dimensional Decisions with Observational Data |
| title_full | Optimization over Continuous and Multi-dimensional Decisions with Observational Data |
| title_fullStr | Optimization over Continuous and Multi-dimensional Decisions with Observational Data |
| title_full_unstemmed | Optimization over Continuous and Multi-dimensional Decisions with Observational Data |
| title_short | Optimization over Continuous and Multi-dimensional Decisions with Observational Data |
| title_sort | optimization over continuous and multi dimensional decisions with observational data |
| url | https://hdl.handle.net/1721.1/137378.2 |
| work_keys_str_mv | AT bertsimasdimitrisj optimizationovercontinuousandmultidimensionaldecisionswithobservationaldata AT mccordchristopher optimizationovercontinuousandmultidimensionaldecisionswithobservationaldata |