Real-Time Optimization in Complex Stochastic Environments

Abstract

The research in this project aims to enable a systematic on-line use of optimization techniques for real-time applications in complex, time-varying stochastic environments. This is in contrast to off-line optimization where computationally intensive methods may be used. The main outcomes of the project are: (a) event-driven distributed optimization algorithms which exploit the spatial decomposition of complex optimization problems and involve minimal communication, (b) receding horizon algorithms which exploit a temporal (rather than spatial) decomposition, (c) a novel "boosting function" approach which takes advantage of structural information to escape local optima and approach global optimality.

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Document Details

Document Type
Technical Report
Publication Date
Dec 14, 2018
Accession Number
AD1085605

Entities

People

  • Christos G. Cassandras

Organizations

  • Boston University

Tags

Communities of Interest

  • Cyber
  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Computational Complexity
  • Computational Science
  • Congress
  • Control Systems
  • Decomposition
  • Environment
  • Flow Network
  • Hybrid Systems
  • Mathematics
  • Multiagent Systems
  • Optimization
  • Probability
  • Sensor Networks
  • Simulations
  • Systems Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Distributed Systems and Data Platform Development