Structural Approach to Distributed Optimization

Abstract

A central component of distributed optimization algorithm design is the case-by-case design of algorithms that solve distributed optimization problems by crafting algorithms that satisfy certain conditions. This research aimed to address this shortcoming. In this research several milestones have been achieved:(a) We showed that distributed optimization algorithms can all be written as a mixture of average tracking dynamics and gradient feedback,(b) We showed that we can relax the fundamental assumption of convexity in several of these works,(c) As for the average tracking for the distributed optimization, we developed tools and techniques to study the averaging dynamics, these tools include infinite flow property, P* chains, and balanced networks,(d) We study a very specific application of distributed optimization and optimization problems to power networks, and we show that relaxation of those problems lead to convex problems with guaranteed performance.

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Document Details

Document Type
Technical Report
Publication Date
Oct 29, 2019
Accession Number
AD1096768

Entities

People

  • Behrouz Touri
  • Fabio Somenzi

Organizations

  • Regents of the University of Colorado

Tags

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Colorado
  • Contracts
  • Department Of Defense
  • Dynamics
  • Energy
  • Energy Management
  • Energy Storage
  • Military Research
  • Model Predictive Control
  • North America
  • Optimization
  • Scientific Research
  • Standards
  • Universities

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Operations Research