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Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
Overview
Presents new, efficient methods for optimization in large-scale multi-agent systems
Develops efficient optimization algorithms for three different information settings in multi-agent systems
Sets optimization problems without common restrictive assumptions
This item is Non-Returnable
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Details
- ISBN-13: 9783319654782
- ISBN-10: 3319654780
- Publisher: Springer
- Publish Date: September 2017
- Dimensions: 9.21 x 6.14 x 0.5 inches
- Shipping Weight: 0.96 pounds
- Page Count: 171
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