01560nam a2200337 i 450099100123285970753620020507113212.0970308s1982 fr ||| | eng 130400b10192025-39ule_instLE00644346ExLDip.to Fisicaitaengfre53(06)53.5.2553.8.8Seminar on thermoluminescence dating4638332 Second specialist Seminar on thermoluminescence dating = Deuxième séminaire de recherches consacrées à la datation par thermoluminescence :Oxford, Research Laboratory for Archaeology and the History of Art, July 1980 /Tony Hackens (ed.)Strasbourg :Council of Europe,1982562 p. :ill. ;27 cm.PACT ;6Seminar on thermoluminescence dating ;2Thermoluminescence datingCongressesHackens, Tonyauthorhttp://id.loc.gov/vocabulary/relators/aut2087272 Deuxième séminaire de recherches consacrées à la datation par thermoluminescence.b1019202517-02-1727-06-02991001232859707536LE006 53(06) HAC12006000083270le006-E0.00-l- 00000.i1023700827-06-02Second specialist Seminar on thermoluminescence dating = Deuxième séminaire de recherches consacrées à la datation par thermoluminescence1445061UNISALENTOle00601-01-97ma -engfr 2105089nam 2200493 450 99646655010331620231110212639.03-030-76275-0(MiAaPQ)EBC6941321(Au-PeEL)EBL6941321(CKB)21435621700041(PPN)261518836(EXLCZ)992143562170004120221110d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAn optimization primer /Johannes O. Royset and Roger J.-B. WetsCham, Switzerland :Springer,[2022]©20221 online resource (692 pages)Springer Series in Operations Research and Financial Engineering Print version: Royset, Johannes O. An Optimization Primer Cham : Springer International Publishing AG,c2022 9783030762742 Includes bibliographical references and index.Intro -- Preface -- How to Read the Book -- Supporting Material -- Acknowledgements -- Contents -- 1 PRELUDE -- 1.A The Mathematical Curtain Rise -- 1.B Data Smoothing -- 1.C Optimization under Uncertainty -- 1.D Convex Analysis -- 1.E Estimation and Classification -- 1.F Gradient Descent Method -- 1.G Newton's Method -- 1.H Acceleration and Regularization -- 1.I Quasi-Newton Methods -- 1.J Coordinate Descent Algorithms -- 2 CONVEX OPTIMIZATION -- 2.A Formulations -- 2.B Subderivatives and Subgradients -- 2.C Subgradient Calculus -- 2.D Proximal Gradient Methods -- 2.E Linear Constraints -- 2.F Karush-Kuhn-Tucker Condition -- 2.G Interior-Point Method -- 2.H Support Vector Machines -- 2.I Subgradient Method -- 2.J Conic Constraints -- 2.K Polyhedral Analysis -- 3 OPTIMIZATION UNDER UNCERTAINTY -- 3.A Product Mix Optimization -- 3.B Expectation Functions -- 3.C Risk Modeling -- 3.D Models of Uncertainty -- 3.E Risk-Adaptive Design -- 3.F Optimality in Stochastic Optimization -- 3.G Stochastic Gradient Descent -- 3.H Simple Recourse Problems -- 3.I Control of Water Pollution -- 3.J Linear Recourse Problems -- 3.K Network Capacity Expansion -- 4 MINIMIZATION PROBLEMS -- 4.A Formulations -- 4.B Network Design and Operation -- 4.C Epigraphical Approximation Algorithm -- 4.D Constraint Softening -- 4.E Set Analysis -- 4.F Robotic Path Planning -- 4.G Tangent and Normal Cones I -- 4.H Tangent and Normal Cones II -- 4.I Subdifferentiability -- 4.J Optimality Conditions -- 4.K SQP and Interior-Point Methods -- 5 PERTURBATION AND DUALITY -- 5.A Rockafellians -- 5.B Quantitative Stability -- 5.C Lagrangians and Dual Problems -- 5.D Lagrangian Relaxation -- 5.E Saddle Points -- 5.F Strong Duality -- 5.G Reformulations -- 5.H L-Shaped Method -- 5.I Monitoring Functions -- 5.J Lagrangian Finite-Generation Method -- 6 WITHOUT CONVEXITY OR SMOOTHNESS.6.A Second-Order Analysis -- 6.B Augmented Lagrangians -- 6.C Epigraphical Nesting -- 6.D Optimality Conditions -- 6.E Sup-Projections -- 6.F Proximal Composite Method -- 6.G Design of Multi-Component Systems -- 6.H Difference-of-Convex Functions -- 6.I DC in Regression and Classification -- 6.J Approximation Errors -- 7 GENERALIZED EQUATIONS -- 7.A Formulations -- 7.B Equilibrium in Energy Markets -- 7.C Traffic Equilibrium -- 7.D Reformulation as Minimization Problems -- 7.E Projection Methods -- 7.F Nonsmooth Newton-Raphson Algorithm -- 7.G Continuity of Set-Valued Mappings -- 7.H Graphical Approximation Algorithm -- 7.I Consistent Approximations -- 7.J Approximation Errors -- 8 RISK MODELING AND SAMPLE AVERAGES -- 8.A Estimation of Optimality Gaps -- 8.B Risk and Regret -- 8.C Risk-Adaptive Data Analytics -- 8.D Duality -- 8.E Subgradients of Functionals -- 8.F Residual Risk and Surrogates -- 8.G Sample Average Approximations -- 8.H Concentration Inequalities -- 8.I Diametrical Stochastic Optimization -- 9 GAMES AND MINSUP PROBLEMS -- 9.A Nash Games -- 9.B Formulation as Minsup Problems -- 9.C Bifunctions and Solutions -- 9.D Lopsided Approximation Algorithm -- 9.E Lop-Convergence I -- 9.F Lop-Convergence II -- 9.G Approximation of Games -- 9.H Walras Barter Model -- 10 DECOMPOSITION -- 10.A Proximal Alternating Gradient Method -- 10.B Linkage Constraints -- 10.C Progressive Decoupling Algorithm -- 10.D Local Elicitation -- 10.E Decoupling in Stochastic Optimization -- 10.F Strong Monotonicity -- 10.G Variational Convexity and Elicitation -- 10.H Nonlinear Linkage -- References -- Index.Springer Series in Operations Research and Financial Engineering Optimització matemàticathubMathematical optimizationLlibres electrònicsthubOptimització matemàticaMathematical optimization.519.6Royset Johannes O.1218704Wets Roger J.-B.MiAaPQMiAaPQMiAaPQBOOK996466550103316An optimization primer2974976UNISA