Online Stochastic Combinatorial Optimization

Description

Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints. Such online decision-making applications are becoming increasingly common: ambulance dispatching and emergency city-evacuation routing, for example, are inherently online decision-making problems; other applications include packet scheduling for Internet communications and reservation systems. This book presents a novel framework, online stochastic optimization, to address this challenge.

This framework assumes that the distribution of future requests, or an approximation thereof, is available for sampling, as is the case in many applications that make either historical data or predictive models available. It assumes additionally that the distribution of future requests is independent of current decisions, which is also the case in a variety of applications and holds significant computational advantages. The book presents several online stochastic algorithms implementing the framework, provides performance guarantees, and demonstrates a variety of applications. It discusses how to relax some of the assumptions in using historical sampling and machine learning and analyzes different underlying algorithmic problems. And finally, the book discusses the framework's possible limitations and suggests directions for future research.



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1. Ebooks list page : 3184
2. Local Search in Combinatorial Optimization (Wiley-Interscience Series in Discrete Mathematics and Optimization)
3. Local Search in Combinatorial Optimization (Wiley-Interscience Series in Discrete Mathematics and Optimization)
4. [request_ebook] Stochastic Optimization: Algorithms and Applications (Applied Optimization, Volume 54)
5. Combinatorial Stochastic Processes (Lecture Notes in Mathematics): Jim Pitman
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7. A First Course in Combinatorial Optimization
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11. Surveys in Combinatorial Optimization
12. Parallel Combinatorial Optimization
13. Combinatorial Optimization for Undergraduates
14. Combinatorial Optimization and Applicati
15. Combinatorial Optimization: Theory and Algorithms
16. Research Trends in Combinatorial Optimization