Linear Convergent Distributed Nash Equilibrium Seeking with Compression Article Swipe
Xiaomeng Chen
,
Yuchi Wu
,
Xinlei Yi
,
Minyi Huang
,
Ling Shi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.07849
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.07849
Information compression techniques are majorly employed to address the concern of reducing communication cost over peer-to-peer links. In this paper, we investigate distributed Nash equilibrium (NE) seeking problems in a class of non-cooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distributed NE seeking (C-DNES) algorithm is proposed to obtain a NE for games, where the differences between decision vectors and their estimates are compressed. The proposed algorithm is compatible with a general class of compression operators, including both unbiased and biased compressors. Moreover, our approach only requires the adjacency matrix of the directed graph to be row-stochastic, in contrast to past works that relied on balancedness or specific global network parameters. It is shown that C-DNES not only inherits the advantages of conventional distributed NE algorithms, achieving linear convergence rate for games with restricted strongly monotone mappings, but also saves communication costs in terms of transmitted bits. Finally, numerical simulations illustrate the advantages of C-DNES in saving communication cost by an order of magnitude under different compressors.
Related Topics
Concepts
Nash equilibrium
Computer science
Monotone polygon
Adjacency matrix
Mathematical optimization
Convergence (economics)
Class (philosophy)
Rate of convergence
Directed graph
Distributed algorithm
Graph
Theoretical computer science
Algorithm
Distributed computing
Mathematics
Computer network
Economics
Economic growth
Artificial intelligence
Channel (broadcasting)
Geometry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.07849
- https://arxiv.org/pdf/2211.07849
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309202186
All OpenAlex metadata
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https://openalex.org/W4309202186Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2211.07849Digital Object Identifier
- Title
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Linear Convergent Distributed Nash Equilibrium Seeking with CompressionWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-11-15Full publication date if available
- Authors
-
Xiaomeng Chen, Yuchi Wu, Xinlei Yi, Minyi Huang, Ling ShiList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.07849Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2211.07849Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2211.07849Direct OA link when available
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Nash equilibrium, Computer science, Monotone polygon, Adjacency matrix, Mathematical optimization, Convergence (economics), Class (philosophy), Rate of convergence, Directed graph, Distributed algorithm, Graph, Theoretical computer science, Algorithm, Distributed computing, Mathematics, Computer network, Economics, Economic growth, Artificial intelligence, Channel (broadcasting), GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.order | 167 |
| abstract_inverted_index.saves | 145 |
| abstract_inverted_index.shown | 119 |
| abstract_inverted_index.terms | 149 |
| abstract_inverted_index.their | 66 |
| abstract_inverted_index.under | 170 |
| abstract_inverted_index.where | 59 |
| abstract_inverted_index.works | 107 |
| abstract_inverted_index.C-DNES | 121, 160 |
| abstract_inverted_index.biased | 86 |
| abstract_inverted_index.games, | 58 |
| abstract_inverted_index.global | 114 |
| abstract_inverted_index.graphs | 36 |
| abstract_inverted_index.linear | 133 |
| abstract_inverted_index.links. | 16 |
| abstract_inverted_index.matrix | 95 |
| abstract_inverted_index.obtain | 54 |
| abstract_inverted_index.paper, | 19 |
| abstract_inverted_index.relied | 109 |
| abstract_inverted_index.saving | 162 |
| abstract_inverted_index.address | 7 |
| abstract_inverted_index.between | 62 |
| abstract_inverted_index.concern | 9 |
| abstract_inverted_index.general | 77 |
| abstract_inverted_index.improve | 41 |
| abstract_inverted_index.majorly | 4 |
| abstract_inverted_index.network | 115 |
| abstract_inverted_index.seeking | 26, 48 |
| abstract_inverted_index.vectors | 64 |
| abstract_inverted_index.(C-DNES) | 49 |
| abstract_inverted_index.Finally, | 153 |
| abstract_inverted_index.approach | 90 |
| abstract_inverted_index.contrast | 104 |
| abstract_inverted_index.decision | 63 |
| abstract_inverted_index.directed | 35, 98 |
| abstract_inverted_index.employed | 5 |
| abstract_inverted_index.inherits | 124 |
| abstract_inverted_index.monotone | 141 |
| abstract_inverted_index.problems | 27 |
| abstract_inverted_index.proposed | 52, 71 |
| abstract_inverted_index.reducing | 11 |
| abstract_inverted_index.requires | 92 |
| abstract_inverted_index.specific | 113 |
| abstract_inverted_index.strongly | 140 |
| abstract_inverted_index.unbiased | 84 |
| abstract_inverted_index.Moreover, | 88 |
| abstract_inverted_index.achieving | 132 |
| abstract_inverted_index.adjacency | 94 |
| abstract_inverted_index.algorithm | 50, 72 |
| abstract_inverted_index.different | 171 |
| abstract_inverted_index.estimates | 67 |
| abstract_inverted_index.including | 82 |
| abstract_inverted_index.magnitude | 169 |
| abstract_inverted_index.mappings, | 142 |
| abstract_inverted_index.numerical | 154 |
| abstract_inverted_index.advantages | 126, 158 |
| abstract_inverted_index.compatible | 74 |
| abstract_inverted_index.compressed | 45 |
| abstract_inverted_index.illustrate | 156 |
| abstract_inverted_index.operators, | 81 |
| abstract_inverted_index.restricted | 139 |
| abstract_inverted_index.techniques | 2 |
| abstract_inverted_index.Information | 0 |
| abstract_inverted_index.algorithms, | 131 |
| abstract_inverted_index.compressed. | 69 |
| abstract_inverted_index.compression | 1, 80 |
| abstract_inverted_index.convergence | 134 |
| abstract_inverted_index.differences | 61 |
| abstract_inverted_index.distributed | 22, 46, 129 |
| abstract_inverted_index.efficiency, | 43 |
| abstract_inverted_index.equilibrium | 24 |
| abstract_inverted_index.information | 38 |
| abstract_inverted_index.investigate | 21 |
| abstract_inverted_index.parameters. | 116 |
| abstract_inverted_index.simulations | 155 |
| abstract_inverted_index.transmitted | 151 |
| abstract_inverted_index.balancedness | 111 |
| abstract_inverted_index.compression. | 39 |
| abstract_inverted_index.compressors. | 87, 172 |
| abstract_inverted_index.conventional | 128 |
| abstract_inverted_index.peer-to-peer | 15 |
| abstract_inverted_index.communication | 12, 42, 146, 163 |
| abstract_inverted_index.non-cooperative | 32 |
| abstract_inverted_index.row-stochastic, | 102 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |