Debasish Roy – författare

This comprehensive textbook covers both classical and geometric aspects of optimization using methods, deterministic and stochastic, in a single volume and in a language accessible to non-mathematicians. It will help serve as an ideal study material for senior undergraduate and graduate students in the fields of civil, mechanical, aerospace, electrical, electronics, and communication engineering.The book includes:Derivative-based Methods of Optimization.Direct Search Methods of Optimization.Basics of Riemannian Differential Geometry.Geometric Methods of Optimization using Riemannian Langevin Dynamics.Stochastic Analysis on Manifolds and Geometric Optimization Methods.This textbook comprehensively treats both classical and geometric optimization methods, including deterministic and stochastic (Monte Carlo) schemes. It offers an extensive coverage of important topics including derivative-based methods, penalty function methods, method of gradient projection, evolutionary methods, geometric search using Riemannian Langevin dynamics and stochastic dynamics on manifolds. The textbook is accompanied by online resources including MATLAB codes which are uploaded on our website. The textbook is primarily written for senior undergraduate and graduate students in all applied science and engineering disciplines and can be used as a main or supplementary text for courses on classical and geometric optimization.

This textbook chart out an easy-to-comprehend account of the methods of random vibrations, aided by modern yet basic concepts in probability theory and random processes. It starts with a quick review of certain elements of structural dynamics, thus setting the stage for their seamless continuation in developing techniques for response analyses of structures under random environmental loads, such as winds and earthquakes. The book also offers a few glimpses of the powerful tools of stochastic processes to kindle the spirit of scientific inquiry. By way of applications, it contains numerous illustrative examples and exercises, many of which relate to practical design problems of interest to the industry. A companion website provides solutions to all the problems in the exercises. For the benefit of the prospective instructors, a semester-long schedule for offering a course on Random Vibrations is also suggested.

This comprehensive textbook covers both classical and geometric aspects of optimization using methods, deterministic and stochastic, in a single volume and in a language accessible to non-mathematicians. It will help serve as an ideal study material for senior undergraduate and graduate students in the fields of civil, mechanical, aerospace, electrical, electronics, and communication engineering.The book includes:Derivative-based Methods of Optimization.Direct Search Methods of Optimization.Basics of Riemannian Differential Geometry.Geometric Methods of Optimization using Riemannian Langevin Dynamics.Stochastic Analysis on Manifolds and Geometric Optimization Methods.This textbook comprehensively treats both classical and geometric optimization methods, including deterministic and stochastic (Monte Carlo) schemes. It offers an extensive coverage of important topics including derivative-based methods, penalty function methods, method of gradient projection, evolutionary methods, geometric search using Riemannian Langevin dynamics and stochastic dynamics on manifolds. The textbook is accompanied by online resources including MATLAB codes which are uploaded on our website. The textbook is primarily written for senior undergraduate and graduate students in all applied science and engineering disciplines and can be used as a main or supplementary text for courses on classical and geometric optimization.

Targeted at graduate students, researchers and practitioners in the field of science and engineering, this book gives a self-contained introduction to a measure-theoretic framework in laying out the definitions and basic concepts of random variables and stochastic diffusion processes. It then continues to weave into a framework of several practical tools and applications involving stochastic dynamical systems. These include tools for the numerical integration of such dynamical systems, nonlinear stochastic filtering and generalized Bayesian update theories for solving inverse problems and a new stochastic search technique for treating a broad class of non-convex optimization problems. MATLAB® codes for all the applications are uploaded on the companion website.

Structural dynamics is a subset of structural analysis which covers the behavior of structures subjected to dynamic loading. The subject has seen rapid growth and also change in how the basic concepts can be interpreted. For instance, the classical notions of discretizing the operator of a dynamic structural model have given way to a set-theoretic, function-space based framework, which is more conducive to implementation with a computer. This modern perspective, as adopted in this book, is also helpful in putting together the various tools and ideas in a more integrated style. Elements of Structural Dynamics: A New Perspective is devoted to covering the basic concepts in linear structural dynamics, whilst emphasizing their mathematical moorings and the associated computational aspects that make their implementation in software possible. Key features: Employs a novel ‘top down’ approach to structural dynamics.Contains an insightful treatment of the  computational aspects, including the finite element method, that translate into numerical solutions of the dynamic equations of motion.Consistently touches upon the modern mathematical basis for the theories and approximations involved.Elements of Structural Dynamics: A New Perspective is a holistic treatise on structural dynamics and is an ideal textbook for senior undergraduate and graduate students in Mechanical, Aerospace and Civil engineering departments. This book also forms a useful reference for researchers and engineers in industry.