Online Convex Optimization Using Predictions Article Swipe
Niangjun Chen
,
Anish K. Agarwal
,
Adam Wierman
,
Siddharth Barman
,
Lachlan L. H. Andrew
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1504.06681
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1504.06681
Making use of predictions is a crucial, but under-explored, area of online algorithms. This paper studies a class of online optimization problems where we have external noisy predictions available. We propose a stochastic prediction error model that generalizes prior models in the learning and stochastic control communities, incorporates correlation among prediction errors, and captures the fact that predictions improve as time passes. We prove that achieving sublinear regret and constant competitive ratio for online algorithms requires the use of an unbounded prediction window in adversarial settings, but that under more realistic stochastic prediction error models it is possible to use Averaging Fixed Horizon Control (AFHC) to simultaneously achieve sublinear regret and constant competitive ratio in expectation using only a constant-sized prediction window. Furthermore, we show that the performance of AFHC is tightly concentrated around its mean.
Related Topics
Concepts
Sublinear function
Regret
Constant (computer programming)
Computer science
Mathematical optimization
Online algorithm
Stochastic optimization
Convex optimization
Class (philosophy)
Competitive analysis
Regular polygon
Mathematics
Algorithm
Artificial intelligence
Machine learning
Upper and lower bounds
Geometry
Mathematical analysis
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1504.06681
- https://arxiv.org/pdf/1504.06681
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307192372
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4307192372Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1504.06681Digital Object Identifier
- Title
-
Online Convex Optimization Using PredictionsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-04-25Full publication date if available
- Authors
-
Niangjun Chen, Anish K. Agarwal, Adam Wierman, Siddharth Barman, Lachlan L. H. AndrewList of authors in order
- Landing page
-
https://arxiv.org/abs/1504.06681Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1504.06681Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1504.06681Direct OA link when available
- Concepts
-
Sublinear function, Regret, Constant (computer programming), Computer science, Mathematical optimization, Online algorithm, Stochastic optimization, Convex optimization, Class (philosophy), Competitive analysis, Regular polygon, Mathematics, Algorithm, Artificial intelligence, Machine learning, Upper and lower bounds, Geometry, Mathematical analysis, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2019: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.crucial, | 6 |
| abstract_inverted_index.external | 25 |
| abstract_inverted_index.learning | 42 |
| abstract_inverted_index.possible | 97 |
| abstract_inverted_index.problems | 21 |
| abstract_inverted_index.requires | 75 |
| abstract_inverted_index.Averaging | 100 |
| abstract_inverted_index.achieving | 65 |
| abstract_inverted_index.realistic | 90 |
| abstract_inverted_index.settings, | 85 |
| abstract_inverted_index.sublinear | 66, 108 |
| abstract_inverted_index.unbounded | 80 |
| abstract_inverted_index.algorithms | 74 |
| abstract_inverted_index.available. | 28 |
| abstract_inverted_index.prediction | 33, 50, 81, 92, 120 |
| abstract_inverted_index.stochastic | 32, 44, 91 |
| abstract_inverted_index.adversarial | 84 |
| abstract_inverted_index.algorithms. | 12 |
| abstract_inverted_index.competitive | 70, 112 |
| abstract_inverted_index.correlation | 48 |
| abstract_inverted_index.expectation | 115 |
| abstract_inverted_index.generalizes | 37 |
| abstract_inverted_index.performance | 127 |
| abstract_inverted_index.predictions | 3, 27, 57 |
| abstract_inverted_index.Furthermore, | 122 |
| abstract_inverted_index.communities, | 46 |
| abstract_inverted_index.concentrated | 132 |
| abstract_inverted_index.incorporates | 47 |
| abstract_inverted_index.optimization | 20 |
| abstract_inverted_index.constant-sized | 119 |
| abstract_inverted_index.simultaneously | 106 |
| abstract_inverted_index.under-explored, | 8 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |