Description

Book Synopsis
This book introduces the Learning-Augmented Network Optimization (LANO) paradigm, which interconnects network optimization with the emerging AI theory and algorithms and has been receiving a growing attention in network research. The authors present the topic based on a general stochastic network optimization model, and review several important theoretical tools that are widely adopted in network research, including convex optimization, the drift method, and mean-field analysis. The book then covers several popular learning-based methods, i.e., learning-augmented drift, multi-armed bandit and reinforcement learning, along with applications in networks where the techniques have been successfully applied. The authors also provide a discussion on potential future directions and challenges.

Table of Contents
Introduction.- The Stochastic Network Model.- Network Optimization Techniques.- Learning Network Decisions.- Summary and Discussions.

Learning for Decision and Control in Stochastic

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    A Hardback by Longbo Huang

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      Publisher: Springer International Publishing AG
      Publication Date: 20/06/2023
      ISBN13: 9783031315961, 978-3031315961
      ISBN10: 3031315960
      Also in:
      Computer networking and communications Probability and statistics Stochastics

      Description

      Book Synopsis
      This book introduces the Learning-Augmented Network Optimization (LANO) paradigm, which interconnects network optimization with the emerging AI theory and algorithms and has been receiving a growing attention in network research. The authors present the topic based on a general stochastic network optimization model, and review several important theoretical tools that are widely adopted in network research, including convex optimization, the drift method, and mean-field analysis. The book then covers several popular learning-based methods, i.e., learning-augmented drift, multi-armed bandit and reinforcement learning, along with applications in networks where the techniques have been successfully applied. The authors also provide a discussion on potential future directions and challenges.

      Table of Contents
      Introduction.- The Stochastic Network Model.- Network Optimization Techniques.- Learning Network Decisions.- Summary and Discussions.

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