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| 001 | 978-3-031-26458-0 | ||
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| 005 | 20240730163708.0 | ||
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_a10.1007/978-3-031-26458-0 _2doi |
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| 100 | 1 |
_aMordukhovich, Boris. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980005 |
|
| 245 | 1 | 3 |
_aAn Easy Path to Convex Analysis and Applications _h[electronic resource] / _cby Boris Mordukhovich, Nguyen Mau Nam. |
| 250 | _a2nd ed. 2023. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2023. |
|
| 300 |
_aXX, 300 p. 35 illus., 31 illus. in color. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aSynthesis Lectures on Mathematics & Statistics, _x1938-1751 |
|
| 505 | 0 | _aConvex Sets and Functions -- Convex Separation and Some Consequences -- Convex Generalized Differentiation -- Fenchel Conjugate and Further Topics In Subdifferentiation -- Remarkable Consequences of Convexity -- Minimal Time Functions and Related Issues -- Applications To Problems of Optimization and Equilibrium -- Applications To Location Problems. | |
| 520 | _aThis book examines the most fundamental parts of convex analysis and its applications to optimization and location problems. Accessible techniques of variational analysis are employed to clarify and simplify some basic proofs in convex analysis and to build a theory of generalized differentiation for convex functions and sets in finite dimensions. The book serves as a bridge for the readers who have just started using convex analysis to reach deeper topics in the field. Detailed proofs are presented for most of the results in the book and also included are many figures and exercises for better understanding the material. Applications provided include both the classical topics of convex optimization and important problems of modern convex optimization, convex geometry, and facility location. In addition, this book: Explains the fundamental theory with an accessible and understandable variational geometric approach; Provides easy access to theoretical and numerical applications to convex optimization and geometry; Simplifies relative interiors of convex sets in developing the theory of generalized differentiation in finite dimensions. | ||
| 650 | 0 |
_aMathematics. _911584 |
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| 650 | 0 |
_aEngineering mathematics. _93254 |
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| 650 | 0 |
_aDynamics. _980006 |
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| 650 | 0 |
_aNonlinear theories. _93339 |
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| 650 | 1 | 4 |
_aMathematics. _911584 |
| 650 | 2 | 4 |
_aEngineering Mathematics. _93254 |
| 650 | 2 | 4 |
_aApplied Dynamical Systems. _932005 |
| 700 | 1 |
_aNam, Nguyen Mau. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980007 |
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_aSpringerLink (Online service) _980008 |
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_iPrinted edition: _z9783031264573 |
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_aSynthesis Lectures on Mathematics & Statistics, _x1938-1751 _980009 |
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