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Convex Optimization - Theory, Methods and Applications (Paperback)
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Convex Optimization - Theory, Methods and Applications (Paperback)
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Over the past two decades, it has been recognized that advanced
image processing techniques provide valuable information to
physicians for the diagnosis, image guided therapy and surgery, and
monitoring of human diseases. This book introduces novel and
sophisticated mathematical problems which encourage the development
of advanced optimisation and computing methods, especially convex
optimisation. The authors go on to study Steffensen-King-type
methods of convergence to approximate a locally unique solution of
a nonlinear equation and also in problems of convex optimisation.
Real-world applications are also provided. The following study is
focused on the design and testing of a Matlab code of the
Frank-Wolfe algorithm. The Nesterov step is proposed in order to
accelerate the algorithm, and the results of some numerical
experiments of constraint optimization are also provided.
Lagrangian methods for numerical solutions to constrained convex
programs are also explored. For enhanced algorithms, the
traditional Lagrange multiplier update is modified to take a soft
reflection across the zero boundary. This, coupled with a modified
drift expression, is shown to yield improved performance. Next,
Newtons mesh independence principle was used to solve a certain
class of optimal design problems from earlier studies. Motivated by
optimization considerations, the authors show that under the same
computational cost, a finer mesh independence principle can be
given than before. This compilation closes with a presentation on a
local convergence analysis for eighthorder variants of
HansenPatricks family for approximating a locally unique solution
of a nonlinear equation. The radius of convergence and computable
error bounds on the distances involved are also provided.
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