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Handbook of Variational Methods for Nonlinear Geometric Data (Paperback, 1st ed. 2020)
Loot Price: R5,554
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Handbook of Variational Methods for Nonlinear Geometric Data (Paperback, 1st ed. 2020)
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This book covers different, current research directions in the
context of variational methods for non-linear geometric data. Each
chapter is authored by leading experts in the respective discipline
and provides an introduction, an overview and a description of the
current state of the art. Non-linear geometric data arises in
various applications in science and engineering. Examples of
nonlinear data spaces are diverse and include, for instance,
nonlinear spaces of matrices, spaces of curves, shapes as well as
manifolds of probability measures. Applications can be found in
biology, medicine, product engineering, geography and computer
vision for instance. Variational methods on the other hand have
evolved to being amongst the most powerful tools for applied
mathematics. They involve techniques from various branches of
mathematics such as statistics, modeling, optimization, numerical
mathematics and analysis. The vast majority of research on
variational methods, however, is focused on data in linear spaces.
Variational methods for non-linear data is currently an emerging
research topic. As a result, and since such methods involve various
branches of mathematics, there is a plethora of different, recent
approaches dealing with different aspects of variational methods
for nonlinear geometric data. Research results are rather scattered
and appear in journals of different mathematical communities. The
main purpose of the book is to account for that by providing, for
the first time, a comprehensive collection of different research
directions and existing approaches in this context. It is organized
in a way that leading researchers from the different fields provide
an introductory overview of recent research directions in their
respective discipline. As such, the book is a unique reference work
for both newcomers in the field of variational methods for
non-linear geometric data, as well as for established experts that
aim at to exploit new research directions or collaborations.
Chapter 9 of this book is available open access under a CC BY 4.0
license at link.springer.com.
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