000			02364nam a22003015i 4500
001			91270
005			20231026103946.0
010			_a978-3-030-31351-7 _dcompra
090			_a91270
100			_a20231023d2020 k||y0pory50 ba
101	0		_aeng
102			_aCH
200	1		_aHandbook of variational methods for nonlinear geometric data _bDocumento eletrónico _fedited by Philipp Grohs, Martin Holler, Andreas Weinmann
210			_aCham _cSpringer International Publishing _cSpringer _d2020
215			_aXXVI, 701 p. _cil.
303			_aThis book explains how variational methods 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.
606			_aMathematics _xData processing
606			_aComputer science _xMathematics
606			_aComputer vision
680			_aQA71-90
702	1		_aGrohs _bPhilipp _4340
702	1		_aHoller _bMartin _4340
702	1		_aWeinmann _bAndreas _4340
801		0	_aPT _gRPC
856	4		_uhttps://doi.org/10.1007/978-3-030-31351-7
942			_2lcc _cF _n0