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008			200513s2020 gw a o |||| 0|eng d
020			_a9783030418465
024	7		_a10.1007/978-3-030-41846-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA169 _b2020 EB
100	1		_aCao, Xi-Ren _eautor _0(orcid)0000-0001-5165-8804 _1https://orcid.org/0000-0001-5165-8804 _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n88681673 _1http://viaf.org/viaf/59771794 _9674892
245	1	0	_aRelative Optimization of Continuous-Time and Continuous-State Stochastic Systems _cby Xi-Ren Cao.
250			_aFirst edition
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2020
300			_a1 recurso en línea (XIX, 365 páginas) _b21 ilustraciones, 12 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aCommunications and Control Engineering _x0178-5354
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aChapter 1. Introduction -- Chapter 2. Optimal Control of Markov Processes: Infinite Horizon -- Chapter 3. Optimal Control of Diffusion Processes -- Chapter 4. Degenerate Diffusion Processes -- Chapter 5. Multi-Dimensional Diffusion Processes -- Chapter 6. Performance-Derivative-Based Optimization -- Appendices -- Index.
520	3		_aThis monograph applies the relative optimization approach to time nonhomogeneous continuous-time and continuous-state dynamic systems. The approach is intuitively clear and does not require deep knowledge of the mathematics of partial differential equations. The topics covered have the following distinguishing features: long-run average with no under-selectivity, non-smooth value functions with no viscosity solutions, diffusion processes with degenerate points, multi-class optimization with state classification, and optimization with no dynamic programming. The book begins with an introduction to relative optimization, including a comparison with the traditional approach of dynamic programming. The text then studies the Markov process, focusing on infinite-horizon optimization problems, and moves on to discuss optimal control of diffusion processes with semi-smooth value functions and degenerate points, and optimization of multi-dimensional diffusion processes. The book concludes with a brief overview of performance derivative-based optimization. Among the more important novel considerations presented are: the extension of the Hamilton-Jacobi-Bellman optimality condition from smooth to semi-smooth value functions by derivation of explicit optimality conditions at semi-smooth points and application of this result to degenerate and reflected processes; proof of semi-smoothness of the value function at degenerate points; attention to the under-selectivity issue for the long-run average and bias optimality; discussion of state classification for time nonhomogeneous continuous processes and multi-class optimization; and development of the multi-dimensional Tanaka formula for semi-smooth functions and application of this formula to stochastic control of multi-dimensional systems with degenerate points. The book will be of interest to researchers and students in the field of stochastic control and performance optimization alike.
988			_aSpringer_Robotics_23062020
650		7	_2embne _aProcesos estocásticos _9405190
710	2		_aSpringerLink (Online service) _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729
776	0	8	_iPrinted edition: _z9783030418458
776	0	8	_iPrinted edition: _z9783030418472
776	0	8	_iPrinted edition: _z9783030418489
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-41846-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2020 _dz _eIG _zSI