000			a
999			_c32408 _d32408
008			230830b xxu||||| |||| 00| 0 eng d
020			_a9783030149765
082			_a519.6 _bBON
100			_aBonnans, J. F.
245			_aConvex and stochastic optimization
260			_bSpringer, _c2019 _aCham :
300			_axiii, 311 p. ; _c24 cm _bill.,
365			_b59.99 _cEUR _d94.90
490			_aUniversitext
504			_aIncludes bibliographical references and index.
520			_aThis textbook provides an introduction to convex duality for optimization problems in Banach spaces, integration theory, and their application to stochastic programming problems in a static or dynamic setting. It introduces and analyses the main algorithms for stochastic programs, while the theoretical aspects are carefully dealt with. The reader is shown how these tools can be applied to various fields, including approximation theory, semidefinite and second-order cone programming and linear decision rules. This textbook is recommended for students, engineers and researchers who are willing to take a rigorous approach to the mathematics involved in the application of duality theory to optimization with uncertainty.
650			_aConvex functions
650			_aAcceptation set
650			_aBounded in probability
650			_aConvex function
650			_aDynamic programming
650			_aFunction moment generating
650			_aHadamard differentiability
650			_aIteration policy
650			_aLegendre transform
650			_aLemma
650			_aProbability
650			_aMeasure theory
942			_2ddc _cBK