This comprehensive textbook covers both classical and geometric aspects of optimization using methods, deterministic and stochastic in a single volume. It will help serve as ideal study material for senior undergraduate and graduate students in the fields of civil, mechanical, electrical, electronics and communication engineering.
This comprehensive textbook covers both classical and geometric aspects of optimization using methods, deterministic and stochastic in a single volume. It will help serve as ideal study material for senior undergraduate and graduate students in the fields of civil, mechanical, electrical, electronics and communication engineering.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Debasish Roy, Professor, Department of Civil Engineering, Indian Institute of Science, Bangalore. He obtained his Ph.D. from the Indian Institute of Science, followed by post-doctoral research at the University of Innsbruck, Austria. He has published over 140 research papers in journals of national and international repute. His current research areas include geometrically inspired and gauge theories for continuum mechanics of solids, non-equilibrium thermodynamics of solids and fluctuation relations valid far from equilibrium, defect engineering and metamaterials with acoustic band gaps and optimization based on stochastic search on Riemannian manifolds. G. Visweswara Rao is currently working as an engineering consultant in Bangalore, India. He received his Ph.D. from the Indian Institute of Science, Bangalore, in 1989. He has published several research papers in the areas of structural dynamics specific to earthquake engineering, nonlinear and random vibration, and structural control. His areas of research include non-linear and stochastic structural dynamics.
Inhaltsangabe
Contents Chapter 1 Optimization methods - A preview 1.1 Introduction 1.2 The continuous case - mathematical formulation 1.3 The discrete case - The travelling salesman problem 1.4 Basics of probability theory and random number generation 1.5 The brachistochrone problem 1.6 More on functional optimization: Hamilton's principle 1.7 Constrained optimization problems and optimality conditions 1.8. Functional optimization and optimal control Concluding Remarks Exercises Notations References Chapter 2 Classical derivative-based methods of optimization 2.1 Introduction 2.2 Basic gradient methods 2.3 Quasi-Newton methods 2.4 Penalty function methods 2.5 Linear programming (LP) 2.6. Method of generalized reduced gradients 2.7 Method of feasible directions 2.8 Method of gradient projection Concluding remarks Exercises Notations References Chapter 3 - Classical derivative-free methods of optimization 3.1 Introduction 3.2 Direct search methods 3.3 Other direct search methods 3.4 Metaheuristics - Evolutionary methods Concluding remarks Exercises Notations References Chapter 4 Elements of Riemannian Differential Geometry and geometric methods of optimization 4.1 Introduction 4.2 Tangent vectors and tangent space on manifolds 4.3 Riemannian (geometric) version of some classical gradient methods 4.4. Statistical estimation by geometrical method of optimization 4.5. Stochastic processes, stochastic calculus and solution of SDEs 4.6. Analogy between statistical sampling and stochastic optimization 4.7. Geometric method of optimization by Riemannian Langevin dynamics Concluding remarks Exercises Notations References Chapter 5 Stochastic analysis on a manifold and more on geometric optimization methods 5.1. Introduction 5.2 Stochastic development on a manifold 5.3. Non-convex function optimization based on stochastic development 5.4. Parameter estimation by GALA Concluding remarks Notations References
Contents Chapter 1 Optimization methods - A preview 1.1 Introduction 1.2 The continuous case - mathematical formulation 1.3 The discrete case - The travelling salesman problem 1.4 Basics of probability theory and random number generation 1.5 The brachistochrone problem 1.6 More on functional optimization: Hamilton's principle 1.7 Constrained optimization problems and optimality conditions 1.8. Functional optimization and optimal control Concluding Remarks Exercises Notations References Chapter 2 Classical derivative-based methods of optimization 2.1 Introduction 2.2 Basic gradient methods 2.3 Quasi-Newton methods 2.4 Penalty function methods 2.5 Linear programming (LP) 2.6. Method of generalized reduced gradients 2.7 Method of feasible directions 2.8 Method of gradient projection Concluding remarks Exercises Notations References Chapter 3 - Classical derivative-free methods of optimization 3.1 Introduction 3.2 Direct search methods 3.3 Other direct search methods 3.4 Metaheuristics - Evolutionary methods Concluding remarks Exercises Notations References Chapter 4 Elements of Riemannian Differential Geometry and geometric methods of optimization 4.1 Introduction 4.2 Tangent vectors and tangent space on manifolds 4.3 Riemannian (geometric) version of some classical gradient methods 4.4. Statistical estimation by geometrical method of optimization 4.5. Stochastic processes, stochastic calculus and solution of SDEs 4.6. Analogy between statistical sampling and stochastic optimization 4.7. Geometric method of optimization by Riemannian Langevin dynamics Concluding remarks Exercises Notations References Chapter 5 Stochastic analysis on a manifold and more on geometric optimization methods 5.1. Introduction 5.2 Stochastic development on a manifold 5.3. Non-convex function optimization based on stochastic development 5.4. Parameter estimation by GALA Concluding remarks Notations References
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