Optimal Online Data-Driven Optimization with Multiple Time-Varying Non-Convex Objectives

Abstract

This report presents the development of optimization research methods and concepts for time-varying, real-world situations, which include: factoring in more realistic assumptions on the dynamical systems underlying the objective functions and constraints, nonconvexity of objectives, reasonability of currently existing performance measures, time-varying constraints, and situations where the true objective function is unknown. Two scenarios were considered for training of statistical models for optimization: 1.) where changes in the objective function were smooth, and 2.) where the dynamical system of objective functions was adversarial (non-predictable). The problems considered in the body of work span a large number of subfields of optimization and are summarized in the remainder of the report.

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Document Details

Document Type
Technical Report
Publication Date
Oct 11, 2019
Accession Number
AD1090195

Entities

People

  • Arthur R. Calderbank
  • Robert Ravier
  • Vahid Tarokh

Organizations

  • Duke University

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Computational Science
  • Data Analysis
  • Data Science
  • Differential Equations
  • Equations
  • Government Procurement
  • Governments
  • Markov Chains
  • Operating Systems
  • Optimization
  • Probability
  • Probability Distributions
  • Random Variables
  • Stochastic Processes

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Operations Research
  • Theoretical Analysis.