Author: PARAMETRIC
Publisher: Birkhäuser
ISBN: 3034862539
Category : Science
Languages : en
Pages : 263
Book Description
Parametric Optimization and Approximation
Author: PARAMETRIC
Publisher: Birkhäuser
ISBN: 3034862539
Category : Science
Languages : en
Pages : 263
Book Description
Publisher: Birkhäuser
ISBN: 3034862539
Category : Science
Languages : en
Pages : 263
Book Description
Parametric Optimization and Approximation
Author: PARAMETRIC
Publisher: Birkhäuser
ISBN: 9783764316716
Category : Science
Languages : en
Pages : 263
Book Description
Publisher: Birkhäuser
ISBN: 9783764316716
Category : Science
Languages : en
Pages : 263
Book Description
Parametric Optimization and Approximation
Author: F. Brosowski
Publisher: Birkhauser
ISBN: 9780817616717
Category :
Languages : en
Pages : 264
Book Description
Publisher: Birkhauser
ISBN: 9780817616717
Category :
Languages : en
Pages : 264
Book Description
Multi-parametric Optimization and Control
Author: Efstratios N. Pistikopoulos
Publisher: John Wiley & Sons
ISBN: 1119265193
Category : Mathematics
Languages : en
Pages : 320
Book Description
Recent developments in multi-parametric optimization and control Multi-Parametric Optimization and Control provides comprehensive coverage of recent methodological developments for optimal model-based control through parametric optimization. It also shares real-world research applications to support deeper understanding of the material. Researchers and practitioners can use the book as reference. It is also suitable as a primary or a supplementary textbook. Each chapter looks at the theories related to a topic along with a relevant case study. Topic complexity increases gradually as readers progress through the chapters. The first part of the book presents an overview of the state-of-the-art multi-parametric optimization theory and algorithms in multi-parametric programming. The second examines the connection between multi-parametric programming and model-predictive control—from the linear quadratic regulator over hybrid systems to periodic systems and robust control. The third part of the book addresses multi-parametric optimization in process systems engineering. A step-by-step procedure is introduced for embedding the programming within the system engineering, which leads the reader into the topic of the PAROC framework and software platform. PAROC is an integrated framework and platform for the optimization and advanced model-based control of process systems. Uses case studies to illustrate real-world applications for a better understanding of the concepts presented Covers the fundamentals of optimization and model predictive control Provides information on key topics, such as the basic sensitivity theorem, linear programming, quadratic programming, mixed-integer linear programming, optimal control of continuous systems, and multi-parametric optimal control An appendix summarizes the history of multi-parametric optimization algorithms. It also covers the use of the parametric optimization toolbox (POP), which is comprehensive software for efficiently solving multi-parametric programming problems.
Publisher: John Wiley & Sons
ISBN: 1119265193
Category : Mathematics
Languages : en
Pages : 320
Book Description
Recent developments in multi-parametric optimization and control Multi-Parametric Optimization and Control provides comprehensive coverage of recent methodological developments for optimal model-based control through parametric optimization. It also shares real-world research applications to support deeper understanding of the material. Researchers and practitioners can use the book as reference. It is also suitable as a primary or a supplementary textbook. Each chapter looks at the theories related to a topic along with a relevant case study. Topic complexity increases gradually as readers progress through the chapters. The first part of the book presents an overview of the state-of-the-art multi-parametric optimization theory and algorithms in multi-parametric programming. The second examines the connection between multi-parametric programming and model-predictive control—from the linear quadratic regulator over hybrid systems to periodic systems and robust control. The third part of the book addresses multi-parametric optimization in process systems engineering. A step-by-step procedure is introduced for embedding the programming within the system engineering, which leads the reader into the topic of the PAROC framework and software platform. PAROC is an integrated framework and platform for the optimization and advanced model-based control of process systems. Uses case studies to illustrate real-world applications for a better understanding of the concepts presented Covers the fundamentals of optimization and model predictive control Provides information on key topics, such as the basic sensitivity theorem, linear programming, quadratic programming, mixed-integer linear programming, optimal control of continuous systems, and multi-parametric optimal control An appendix summarizes the history of multi-parametric optimization algorithms. It also covers the use of the parametric optimization toolbox (POP), which is comprehensive software for efficiently solving multi-parametric programming problems.
Approximation Methods for Multiobjective and Parametric Optimization Problems
Author: Arne Herzel
Publisher:
ISBN: 9783843950060
Category :
Languages : en
Pages :
Book Description
Publisher:
ISBN: 9783843950060
Category :
Languages : en
Pages :
Book Description
Simulation-Based Optimization
Author: Abhijit Gosavi
Publisher: Springer
ISBN: 1489974911
Category : Business & Economics
Languages : en
Pages : 530
Book Description
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.
Publisher: Springer
ISBN: 1489974911
Category : Business & Economics
Languages : en
Pages : 530
Book Description
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.
Parametric Optimization
Author: Jürgen Guddat
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 208
Book Description
Explores optimization problems in which some or all of the individual data involved depends on one parameter. Beginning with a preliminary survey of solution algorithms in one-parametric optimization, the text moves on to examine the pathfollowing curves of local minimizers, pathfollowing along a connected component in the Karush-Kuhn-Tucker set and in the critical set, pathfollowing in the set of local minimizers and in the set of critical points. In addition, practical applications are included.
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 208
Book Description
Explores optimization problems in which some or all of the individual data involved depends on one parameter. Beginning with a preliminary survey of solution algorithms in one-parametric optimization, the text moves on to examine the pathfollowing curves of local minimizers, pathfollowing along a connected component in the Karush-Kuhn-Tucker set and in the critical set, pathfollowing in the set of local minimizers and in the set of critical points. In addition, practical applications are included.
An Efficient, Strongly Polynomial, E-approximation Parametric Optimization Scheme
Author: S.N. Kabadi
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages : 9
Book Description
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages : 9
Book Description
Parametric Optimization and Related Topics
Author:
Publisher:
ISBN:
Category : Mathematical optimization
Languages : en
Pages : 212
Book Description
Publisher:
ISBN:
Category : Mathematical optimization
Languages : en
Pages : 212
Book Description
Non-Linear Parametric Optimization
Author: BANK
Publisher: Birkhäuser
ISBN:
Category : Juvenile Nonfiction
Languages : en
Pages : 236
Book Description
Publisher: Birkhäuser
ISBN:
Category : Juvenile Nonfiction
Languages : en
Pages : 236
Book Description