Sequential Quadratic Hamiltonian Method

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A01=Alfio Borzi
Adjoint Problem
Adjoint Variable
Author_Alfio Borzi
Category=PBKJ
Category=PBKS
Category=PBT
Category=PBU
Category=PBW
Category=UYA
Closed Loop Control
Cost Functionals
deep learning control applications
differential equations control
Differential Game
differential models
differential Nash game problems
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eq_computing
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eq_isMigrated=2
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Explicit Euler Scheme
Fokker Planck Type Equations
Friedrichs Inequality
hamiltonian method
HJB Equation
inverse PDE problems
Liouville Equation
Nash equilibrium games
Nash Games
Ne
Ne Point
numerical optimization
ODE control problems
Open Loop Case
Open Loop Control
Optimal Control
Optimal Control Function
Optimal Control Problems
PDE constrained optimization
Pontryagin maximum principle
Relaxed Control
SDE Model
stochastic control theory
Sufficient Decrease Condition
Supervised Learning Problem
Weight Sequence
Young Measures

Product details

  • ISBN 9780367715526
  • Weight: 780g
  • Dimensions: 156 x 234mm
  • Publication Date: 26 May 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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The sequential quadratic hamiltonian (SQH) method is a novel numerical optimization procedure for solving optimal control problems governed by differential models. It is based on the characterisation of optimal controls in the framework of the Pontryagin maximum principle (PMP).

The SQH method is a powerful computational methodology that is capable of development in many directions. The Sequential Quadratic Hamiltonian Method: Solving Optimal Control Problems discusses its analysis and use in solving nonsmooth ODE control problems, relaxed ODE control problems, stochastic control problems, mixed-integer control problems, PDE control problems, inverse PDE problems, differential Nash game problems, and problems related to residual neural networks.

This book may serve as a textbook for undergraduate and graduate students, and as an introduction for researchers in sciences and engineering who intend to further develop the SQH method or wish to use it as a numerical tool for solving challenging optimal control problems and for investigating the Pontryagin maximum principle on new optimisation problems.

Features

    • Provides insight into mathematical and computational issues concerning optimal control problems, while discussing many differential models of interest in different disciplines.
    • Suitable for undergraduate and graduate students and as an introduction for researchers in sciences and engineering.
    • Accompanied by codes which allow the reader to apply the SQH method to solve many different optimal control and optimisation problems.

      Alfio Borzì, born 1965 in Catania (Italy), is Professor and Chair of Scientific Computing at the Institute for Mathematics of the University of Würzburg, Germany. He studied Mathematics and Physics in Catania and Trieste where he received his PhD in Mathematics from Scuola Internazionale Superiore di Studi Avanzati (SISSA).

      He served as Research Officer at the University of Oxford (UK) and as Assistant Professor at the University of Graz (Austria) where he completed his Habilitation and was appointed as Associate Professor. Since 2011 he has been Professor of Scientific Computing at the University of Würzburg.

      Alfio Borzì is author of 4 mathematics books and numerous articles in scientific journals. The main topics of his research and teaching activities are modelling and numerical analysis, optimal control, optimisation, and scientific computing. He is member of the editorial board for SIAM Review.

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