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Techniques of optimization are applied in many problems in
economics, automatic control, engineering, etc. and a wealth of
literature is devoted to this subject. The first computer
applications involved linear programming problems with simp- le
structure and comparatively uncomplicated nonlinear pro- blems:
These could be solved readily with the computational power of
existing machines, more than 20 years ago. Problems of increasing
size and nonlinear complexity made it necessa- ry to develop a
complete new arsenal of methods for obtai- ning numerical results
in a reasonable time. The lineariza- tion method is one of the
fruits of this research of the last 20 years. It is closely related
to Newton's method for solving systems of linear equations, to
penalty function me- thods and to methods of nondifferentiable
optimization. It requires the efficient solution of quadratic
programming problems and this leads to a connection with conjugate
gra- dient methods and variable metrics. This book, written by one
of the leading specialists of optimization theory, sets out to
provide - for a wide readership including engineers, economists and
optimization specialists, from graduate student level on - a brief
yet quite complete exposition of this most effective method of
solution of optimization problems.
Adaptive systems are widely encountered in many applications
ranging through adaptive filtering and more generally adaptive
signal processing, systems identification and adaptive control, to
pattern recognition and machine intelligence: adaptation is now
recognised as keystone of "intelligence" within computerised
systems. These diverse areas echo the classes of models which
conveniently describe each corresponding system. Thus although
there can hardly be a "general theory of adaptive systems"
encompassing both the modelling task and the design of the
adaptation procedure, nevertheless, these diverse issues have a
major common component: namely the use of adaptive algorithms, also
known as stochastic approximations in the mathematical statistics
literature, that is to say the adaptation procedure (once all
modelling problems have been resolved). The juxtaposition of these
two expressions in the title reflects the ambition of the authors
to produce a reference work, both for engineers who use these
adaptive algorithms and for probabilists or statisticians who would
like to study stochastic approximations in terms of problems
arising from real applications. Hence the book is organised in two
parts, the first one user-oriented, and the second providing the
mathematical foundations to support the practice described in the
first part. The book covers the topcis of convergence, convergence
rate, permanent adaptation and tracking, change detection, and is
illustrated by various realistic applications originating from
these areas of applications.
"You ever hunt werewolf, Whitney?" It is 1903 and that startling
question is only one of many posed to young zoologist Gerard
Whitney by eccentric Oklahoma cattle rancher Harold B. Tucker. When
hired by Tucker he expects only to study wildlife on the ranch, but
instead finds himself caught up in Tucker's true passion, searching
the world for legendary creatures. And the thing Tucker wants most?
To find a living dinosaur As they embark on one wild and dangerous
adventure after another, Whitney is sure Tucker will never succeed.
But what they eventually discover exceeds both men's wildest dreams
- and nightmares.
Techniques of optimization are applied in many problems in
economics, automatic control, engineering, etc. and a wealth of
literature is devoted to this subject. The first computer
applications involved linear programming problems with simp- le
structure and comparatively uncomplicated nonlinear pro- blems:
These could be solved readily with the computational power of
existing machines, more than 20 years ago. Problems of increasing
size and nonlinear complexity made it necessa- ry to develop a
complete new arsenal of methods for obtai- ning numerical results
in a reasonable time. The lineariza- tion method is one of the
fruits of this research of the last 20 years. It is closely related
to Newton's method for solving systems of linear equations, to
penalty function me- thods and to methods of nondifferentiable
optimization. It requires the efficient solution of quadratic
programming problems and this leads to a connection with conjugate
gra- dient methods and variable metrics. This book, written by one
of the leading specialists of optimization theory, sets out to
provide - for a wide readership including engineers, economists and
optimization specialists, from graduate student level on - a brief
yet quite complete exposition of this most effective method of
solution of optimization problems.
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