Result: An inexact deflected subgradient algorithm with applications to optimal control problems

Title:
An inexact deflected subgradient algorithm with applications to optimal control problems
Contributors:
Liu, Xuemei, University of South Australia. UniSA STEM.
Publisher Information:
2022.
Publication Year:
2022
Subject Terms:
generalized augmented Lagrangian, deflected subgradient algorithm, Nonlinear control theory, optimal control problems, Lagrange equations, Computer algorithms
Document Type:
Dissertation/ Thesis Thesis
Language:
English
Access URL:
https://hdl.handle.net/11541.2/29638
Accession Number:
edsair.od......1231..5c3d0df584df20ad1f9dfe5aca81a15e
Database:
OpenAIRE

Further Information

Thesis (PhD(Mathematics and Statistics))--University of South Australia, 2022. Includes bibliographical references (pages 153-161) The augmented Lagrangian primal-dual framework is a powerful tool for solving nonconvex optimization problems. In this work first we provide a general Lagrangian in the primal-dual framework for solving nonconvex and nonsmooth optimization problems in infinite dimensions. Then we design a deflected subgradient algorithm using our primal-dual framework and solve optimal control problems using the framework and the algorithm.