Deterministic Global Optimization, Christodoulos A. Floudas
Автор: Daniel Scholz Название: Deterministic Global Optimization ISBN: 1489995552 ISBN-13(EAN): 9781489995551 Издательство: Springer Рейтинг: Цена: 16070.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book examines geometric branch-and-bound methods, such as in Lipschitzian optimization, d.c. programming and interval analysis, introduces a new concept for the rate of convergence and also analyzes several bounding operations reported in the literature.
Автор: Christodoulos A. Floudas Название: Deterministic Global Optimization ISBN: 0792360141 ISBN-13(EAN): 9780792360148 Издательство: Springer Рейтинг: Цена: 58557.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides a unified treatment of deterministic global optimization. This book introduces theoretical and algorithmic advances that address the computation and characterization of global optima, determine valid lower and upper bounds on the global minima and maxima, and enclose all solutions of nonlinear constrained systems of equations.
Автор: Panos M. Pardalos; Anatoly Zhigljavsky; Julius ?il Название: Advances in Stochastic and Deterministic Global Optimization ISBN: 3319299735 ISBN-13(EAN): 9783319299730 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Current research results in stochastic and deterministic global optimization including single and multiple objectives are explored and presented in this book by leading specialists from various fields. Contributions include applications to multidimensional data visualization, regression, survey calibration, inventory management, timetabling, chemical engineering, energy systems, and competitive facility location. Graduate students, researchers, and scientists in computer science, numerical analysis, optimization, and applied mathematics will be fascinated by the theoretical, computational, and application-oriented aspects of stochastic and deterministic global optimization explored in this book.
This volume is dedicated to the 70th birthday of Antanas ?ilinskas who is a leading world expert in global optimization. Professor ?ilinskas's research has concentrated on studying models for the objective function, the development and implementation of efficient algorithms for global optimization with single and multiple objectives, and application of algorithms for solving real-world practical problems.
Mark H.A. Davis introduced the Piecewise-Deterministic Markov Process (PDMP) class of stochastic hybrid models in an article in 1984. Today it is used to model a variety of complex systems in the fields of engineering, economics, management sciences, biology, Internet traffic, networks and many more. Yet, despite this, there is very little in the way of literature devoted to the development of numerical methods for PDMDs to solve problems of practical importance, or the computational control of PDMPs.
This book therefore presents a collection of mathematical tools that have been recently developed to tackle such problems. It begins by doing so through examples in several application domains such as reliability. The second part is devoted to the study and simulation of expectations of functionals of PDMPs. Finally, the third part introduces the development of numerical techniques for optimal control problems such as stopping and impulse control problems.
Preface.- Part 1: Theoretical and Numerical Methods and Tools for Optimization.- 1.1 Theoretical Methods and Tools.- 1.1.1 Multi-Objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges, by Carlos A. Coello Coello.- 1.1.2 Hybrid Optimization Algorithms and Hybrid Response Surfaces, by George S. Dulikravich and Marcelo J. Colaco.- 1.1.3 A genetic algorithm for a sensor device location problem, by Egidio D'Amato, Elia Daniele and Lina Mallozzi.- 1.1.4 The Role of Artificial Neural Networks in Evolutionary Optimization: A Review, by Mustapha Maarouf, Adriel Sosa, Blas Galvбn, David Greiner, Gabriel Winter, Mбximo Mendez and Ricardo Aguasca.- 1.1.5 Reliability-based Design Optimization with the Generalized Inverse Distribution Function, by Domenico Quagliarella, Giovanni Petrone and Gianluca Iaccarino.- 1.2 Numerical Methods and Tools.- 1.2.1 On the choice of surrogates for multilevel aircraft performance models, by Manon Bondouy, Sophie Jan, Serge Laporte and Christian Bes.- 1.2.2 Multi-objective design optimization using high-order statistics for CFD applications, by Pietro M. Congedo, Gianluca Geraci, Remi Abgrall and Gianluca Iaccarino.- 1.2.3 Extension of the One-Shot Method for Optimal Control with Unsteady PDEs, by Stefanie Gunther, Nicolas R. Gauger and Qiqi Wang.- 1.2.4 Adaptive Aerodynamic Design Optimization for Navier-Stokes using Shape Derivatives with Discontinuous Galerkin Methods, by Lena Kaland, Matthias Sonntag and Nicolas R. Gauger.- 1.2.5 Optimal Flow Control and Topology Optimization Using the Continuous Adjoint Method in Unsteady Flows, by Ioannis S. Kavvadias, George K. Karpouzas, Evangelos M. Paoutsis-Kiachagias, Dimitris I. Papadimitrou and Kyriakos C. Giannakoglou.- Part 2: Engineering Design and Societal Applications.- 2.1 Turbomachinery.- 2.1.1 Design optimization of the Primary Pump of a Nuclear Reactor, by Tom Verstraete and Lasse Mueller.- 2.1.2 Direct 3D Aerodynamic Optimization of Turbine Blades with GPU-accelerated CFD, by Philipp Amtsfeld, Dieter Bestle and Marcus Meyer.- 2.1.3 Evaluation of Surrogate Modelling Methods for Turbo-Machinery Component Design Optimization, by Gianluca Badjan, Carlo Poloni, Andrew Pike and Nadir Ince.- 2.1.4 Robust Aerodynamic Design Optimization of Horizontal Axis Wind Turbine Rotors, by Marco Caboni, Edmondo Minisci and Michele Sergio Campobaso.- 2.1.5 Horizontal axis hydroturbine shroud airfoil optimization, by Elia Daniele, Elios Ferrauto and Domenico P. Coiro.- 2.1.6 Parametric Blending and FE-Optimization of a Compressor Blisk Test Case, by Kai Karger and Dieter Bestle.- 2.1.7 Modular Automated Aerodynamic Compressor Design Process, by Fiete Poehlmann, Dieter Bestle, Peter Flassig and Michиl Hinz.- 2.1.8 Design-Optimization of a Compressor Blading on a GPU Cluster, by Konstantinos T. Tsiakas, Xenofon S. Trompoukis, Varvara G. Asouti and Kyriakos C. Giannakoglou.- 2.2 Structures, Materials and Civil Engineering.- 2.2.1 Immune and Swarm Optimization of Structures, by Tadeusz Burczyński, Arkadiusz Poteralski and Miroslaw Szczepanik.- 2.2.2 Investigation of three genotypes for mixed variable evolutionary optimization, by Rajan Filomeno Coelho, Manyu Xiao, Aurore Guglielmetti, Manuel Herrera and Weihong Zhang.- 2.2.3 A Study of Nash-Evolutionary Algorithms for Reconstruction Inverse Problems in Structural Engineering, by David Greiner, Jacques Pйriaux, Josй Marнa Emperador, Blas Galvбn and Gabriel Winter.- 2.2.4 A comparative study on design optimization of polygonal and Bйzier curve-shaped thin noise barriers using dual BEMformulation, by Rayco Toledo, Juan J. Aznбrez, Orlando Maeso and David Greiner.- 2.2.5 A Discrete Adjoint Approach For Trailing-Edge Noise Minimization using Porous Material, by Beckett Y. Zhou, Nicolas R. Gauger, Seong R. Koh and Wolfgang Schrцder.- 2.3 Aeronautics and Astronautics.- 2.3.1 Conceptual Design of Single-Stage Launch Vehicle with Hybrid Rocket Engine Using Desi
Preface.- Part 1: Theoretical and Numerical Methods and Tools for Optimization.- 1.1 Theoretical Methods and Tools.- 1.1.1 Multi-Objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges, by Carlos A. Coello Coello.- 1.1.2 Hybrid Optimization Algorithms and Hybrid Response Surfaces, by George S. Dulikravich and Marcelo J. Colaco.- 1.1.3 A genetic algorithm for a sensor device location problem, by Egidio D'Amato, Elia Daniele and Lina Mallozzi.- 1.1.4 The Role of Artificial Neural Networks in Evolutionary Optimization: A Review, by Mustapha Maarouf, Adriel Sosa, Blas Galvбn, David Greiner, Gabriel Winter, Mбximo Mendez and Ricardo Aguasca.- 1.1.5 Reliability-based Design Optimization with the Generalized Inverse Distribution Function, by Domenico Quagliarella, Giovanni Petrone and Gianluca Iaccarino.- 1.2 Numerical Methods and Tools.- 1.2.1 On the choice of surrogates for multilevel aircraft performance models, by Manon Bondouy, Sophie Jan, Serge Laporte and Christian Bes.- 1.2.2 Multi-objective design optimization using high-order statistics for CFD applications, by Pietro M. Congedo, Gianluca Geraci, Remi Abgrall and Gianluca Iaccarino.- 1.2.3 Extension of the One-Shot Method for Optimal Control with Unsteady PDEs, by Stefanie Gunther, Nicolas R. Gauger and Qiqi Wang.- 1.2.4 Adaptive Aerodynamic Design Optimization for Navier-Stokes using Shape Derivatives with Discontinuous Galerkin Methods, by Lena Kaland, Matthias Sonntag and Nicolas R. Gauger.- 1.2.5 Optimal Flow Control and Topology Optimization Using the Continuous Adjoint Method in Unsteady Flows, by Ioannis S. Kavvadias, George K. Karpouzas, Evangelos M. Paoutsis-Kiachagias, Dimitris I. Papadimitrou and Kyriakos C. Giannakoglou.- Part 2: Engineering Design and Societal Applications.- 2.1 Turbomachinery.- 2.1.1 Design optimization of the Primary Pump of a Nuclear Reactor, by Tom Verstraete and Lasse Mueller.- 2.1.2 Direct 3D Aerodynamic Optimization of Turbine Blades with GPU-accelerated CFD, by Philipp Amtsfeld, Dieter Bestle and Marcus Meyer.- 2.1.3 Evaluation of Surrogate Modelling Methods for Turbo-Machinery Component Design Optimization, by Gianluca Badjan, Carlo Poloni, Andrew Pike and Nadir Ince.- 2.1.4 Robust Aerodynamic Design Optimization of Horizontal Axis Wind Turbine Rotors, by Marco Caboni, Edmondo Minisci and Michele Sergio Campobaso.- 2.1.5 Horizontal axis hydroturbine shroud airfoil optimization, by Elia Daniele, Elios Ferrauto and Domenico P. Coiro.- 2.1.6 Parametric Blending and FE-Optimization of a Compressor Blisk Test Case, by Kai Karger and Dieter Bestle.- 2.1.7 Modular Automated Aerodynamic Compressor Design Process, by Fiete Poehlmann, Dieter Bestle, Peter Flassig and Michиl Hinz.- 2.1.8 Design-Optimization of a Compressor Blading on a GPU Cluster, by Konstantinos T. Tsiakas, Xenofon S. Trompoukis, Varvara G. Asouti and Kyriakos C. Giannakoglou.- 2.2 Structures, Materials and Civil Engineering.- 2.2.1 Immune and Swarm Optimization of Structures, by Tadeusz Burczyński, Arkadiusz Poteralski and Miroslaw Szczepanik.- 2.2.2 Investigation of three genotypes for mixed variable evolutionary optimization, by Rajan Filomeno Coelho, Manyu Xiao, Aurore Guglielmetti, Manuel Herrera and Weihong Zhang.- 2.2.3 A Study of Nash-Evolutionary Algorithms for Reconstruction Inverse Problems in Structural Engineering, by David Greiner, Jacques Pйriaux, Josй Marнa Emperador, Blas Galvбn and Gabriel Winter.- 2.2.4 A comparative study on design optimization of polygonal and Bйzier curve-shaped thin noise barriers using dual BEMformulation, by Rayco Toledo, Juan J. Aznбrez, Orlando Maeso and David Greiner.- 2.2.5 A Discrete Adjoint Approach For Trailing-Edge Noise Minimization using Porous Material, by Beckett Y. Zhou, Nicolas R. Gauger, Seong R. Koh and Wolfgang Schrцder.- 2.3 Aeronautics and Astronautics.- 2.3.1 Conceptual Design of Single-Stage Launch Vehicle with Hybrid Rocket Engine Using Desi
Описание: This IMA Volume in Mathematics and its Applications NONSMOOTH ANALYSIS AND GEOMETRIC METHODS IN DETERMINISTIC OPTIMAL CONTROL is based on the proceedings of a workshop that was an integral part of the 1992-93 IMA program on "Control Theory.
Автор: Wendell H. Fleming; Raymond W. Rishel Название: Deterministic and Stochastic Optimal Control ISBN: 1461263824 ISBN-13(EAN): 9781461263821 Издательство: Springer Рейтинг: Цена: 15366.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Kevin L. Moore Название: Iterative Learning Control for Deterministic Systems ISBN: 1447119142 ISBN-13(EAN): 9781447119142 Издательство: Springer Рейтинг: Цена: 13060.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The text follows with a complete and unifying analysis of the learning control problem for linear LTI systems using a system-theoretic approach which offers insight into the nature of the solution of the learning control problem.
Описание: The most important theoretical aspects of Image and Signal Processing (ISP) for both deterministic and random signals, the theory being supported by exercises and computer simulations relating to real applications.
Описание: Presents a combination of solutions to Maxwell`s equations with conservation of energy to solve for the statistics of many quantities of interest: penetration into cavities (and shielding effectiveness), field strengths far from and close to cavity walls, and power received by antennas within cavities.
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