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""Long Term Durability of Structural Materials"" features
proceedings of the workshop held at Berkeley, CA in October, 2000.
It brought together engineers and scientists, who have received
grants from the initiative NSF 98-42, to share their results on the
study of long-term durability of materials and structures.
The major objective was to develop new methods for accelerated
short-term laboratory or in-situ tests which allow accurate,
reliable, predictions of the long-term performance of materials,
machines and structures. To achieve this goal it was important to
understand the fundamental nature of the deterioration and damage
processes in materials and to develop innovative ways to model the
behavior of these processes as they affect the life and long-term
performance of components, machines and structures.
The researchers discussed their approach to include size effects
in scaling up from laboratory specimens to actual structures.
Accelerated testing and durability modeling techniques developed
were validated by comparing their results with performance under
actual operating conditions. The main mechanism of the
deterioration discussed included environmental effects and/or
exposure to loads, speeds and other operating conditions that are
not fully anticipated in the original design. A broad range of
deterioration damage, such as fatigue, overload, ultraviolet
damage, corrosion, and wear was presented.
A broad range of materials of interest was also discussed,
including the full spectrum of construction materials, metals,
ceramics, polymers, composites, and coatings. Emphasis was placed
on scale-dependence and history of fabrication on resulting
mechanical behavior of materials.
Accessible introductory textbook on optimization theory and
methods, with an emphasis on engineering design, featuring MATLAB
exercises and worked examples Fully updated to reflect modern
developments in the field, the Fifth Edition of An Introduction to
Optimization fills the need for an accessible, yet rigorous,
introduction to optimization theory and methods, featuring
innovative coverage and a straightforward approach. The book begins
with a review of basic definitions and notations while also
providing the related fundamental background of linear algebra,
geometry, and calculus. With this foundation, the authors explore
the essential topics of unconstrained optimization problems, linear
programming problems, and nonlinear constrained optimization. In
addition, the book includes an introduction to artificial neural
networks, convex optimization, multi-objective optimization, and
applications of optimization in machine learning. Numerous diagrams
and figures found throughout the book complement the written
presentation of key concepts, and each chapter is followed by
MATLAB® exercises and practice problems that reinforce the
discussed theory and algorithms. The Fifth Edition features a new
chapter on Lagrangian (nonlinear) duality, expanded coverage on
matrix games, projected gradient algorithms, machine learning, and
numerous new exercises at the end of each chapter. An Introduction
to Optimization includes information on: The mathematical
definitions, notations, and relations from linear algebra,
geometry, and calculus used in optimization Optimization
algorithms, covering one-dimensional search, randomized search, and
gradient, Newton, conjugate direction, and quasi-Newton methods
Linear programming methods, covering the simplex algorithm,
interior point methods, and duality Nonlinear constrained
optimization, covering theory and algorithms, convex optimization,
and Lagrangian duality Applications of optimization in machine
learning, including neural network training, classification,
stochastic gradient descent, linear regression, logistic
regression, support vector machines, and clustering. An
Introduction to Optimization is an ideal textbook for a one- or
two-semester senior undergraduate or beginning graduate course in
optimization theory and methods. The text is also of value for
researchers and professionals in mathematics, operations research,
electrical engineering, economics, statistics, and business.
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