01220nam0 2200301 i 450 SUN013271120210409120052.7150.00N978-1-4614-8766-120210324d2014 |0engc50 baengUS|||| |||||*From Casual Stargazer to Amateur AstronomerHow to Advance to the Next LevelDave EagleNew York : Springer, 2014xvi258 p.ill. ; 24 cmPubblicazione in formato elettronico001SUN01326882001 The *Patrick Moore Practical Astronomy Series210 New YorkSpringer.85-XXAstronomy and Astrophysics [MSC 2020]MFSUNC023246USNew YorkSUNL000011Eagle, DaveSUNV106531791312SpringerSUNV000178650ITSOL20210503RICAhttp://doi.org/10.1007/978-1-4614-8766-1SUN0132711UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA08CONS e-book 1918 08eMF1918 20210324 From Casual Stargazer to Amateur Astronomer1768674UNICAMPANIA05615nam 2200769Ia 450 991081452140332120200520144314.09786612707773978128270777112827077799780470640425047064042197804706404180470640413(CKB)2670000000035102(EBL)565130(OCoLC)669166165(SSID)ssj0000416885(PQKBManifestationID)11278865(PQKBTitleCode)TC0000416885(PQKBWorkID)10423377(PQKB)11327991(MiAaPQ)EBC565130(Au-PeEL)EBL565130(CaPaEBR)ebr10419375(CaONFJC)MIL270777(PPN)243482264(OCoLC)500823432(FINmELB)ELB179313(Perlego)2759391(EXLCZ)99267000000003510220100218d2010 uy 0engur|n|---|||||txtccrEngineering optimization an introduction with metaheuristic applications /Xin-She Yang1st ed.Hoboken, NJ Wileyc20101 online resource (377 p.)Description based upon print version of record.9780470582466 0470582464 Includes bibliographical references and index.Engineering Optimization: An Introduction with Metaheuristic Applications; CONTENTS; List of Figures; Preface; Acknowledgments; Introduction; PART I FOUNDATIONS OF OPTIMIZATION AND ALGORITHMS; 1 A Brief History of Optimization; 1.1 Before 1900; 1.2 Twentieth Century; 1.3 Heuristics and Metaheuristics; Exercises; 2 Engineering Optimization; 2.1 Optimization; 2.2 Type of Optimization; 2.3 Optimization Algorithms; 2.4 Metaheuristics; 2.5 Order Notation; 2.6 Algorithm Complexity; 2.7 No Free Lunch Theorems; Exercises; 3 Mathematical Foundations; 3.1 Upper and Lower Bounds; 3.2 Basic Calculus3.3 Optimality3.3.1 Continuity and Smoothness; 3.3.2 Stationary Points; 3.3.3 Optimality Criteria; 3.4 Vector and Matrix Norms; 3.5 Eigenvalues and Definiteness; 3.5.1 Eigenvalues; 3.5.2 Definiteness; 3.6 Linear and Affine Functions; 3.6.1 Linear Functions; 3.6.2 Affine Functions; 3.6.3 Quadratic Form; 3.7 Gradient and Hessian Matrices; 3.7.1 Gradient; 3.7.2 Hessian; 3.7.3 Function approximations; 3.7.4 Optimality of multivariate functions; 3.8 Convexity; 3.8.1 Convex Set; 3.8.2 Convex Functions; Exercises; 4 Classic Optimization Methods I; 4.1 Unconstrained Optimization4.2 Gradient-Based Methods4.2.1 Newton's Method; 4.2.2 Steepest Descent Method; 4.2.3 Line Search; 4.2.4 Conjugate Gradient Method; 4.3 Constrained Optimization; 4.4 Linear Programming; 4.5 Simplex Method; 4.5.1 Basic Procedure; 4.5.2 Augmented Form; 4.6 Nonlinear Optimization; 4.7 Penalty Method; 4.8 Lagrange Multipliers; 4.9 Karush-Kuhn-Tucker Conditions; Exercises; 5 Classic Optimization Methods II; 5.1 BFGS Method; 5.2 Nelder-Mead Method; 5.2.1 A Simplex; 5.2.2 Nelder-Mead Downhill Simplex; 5.3 Trust-Region Method; 5.4 Sequential Quadratic Programming; 5.4.1 Quadratic Programming5.4.2 Sequential Quadratic ProgrammingExercises; 6 Convex Optimization; 6.1 KKT Conditions; 6.2 Convex Optimization Examples; 6.3 Equality Constrained Optimization; 6.4 Barrier Functions; 6.5 Interior-Point Methods; 6.6 Stochastic and Robust Optimization; Exercises; 7 Calculus of Variations; 7.1 Euler-Lagrange Equation; 7.1.1 Curvature; 7.1.2 Euler-Lagrange Equation; 7.2 Variations with Constraints; 7.3 Variations for Multiple Variables; 7.4 Optimal Control; 7.4.1 Control Problem; 7.4.2 Pontryagin's Principle; 7.4.3 Multiple Controls; 7.4.4 Stochastic Optimal Control; Exercises8 Random Number Generators8.1 Linear Congruential Algorithms; 8.2 Uniform Distribution; 8.3 Other Distributions; 8.4 Metropolis Algorithms; Exercises; 9 Monte Carlo Methods; 9.1 Estimating π; 9.2 Monte Carlo Integration; 9.3 Importance of Sampling; Exercises; 10 Random Walk and Markov Chain; 10.1 Random Process; 10.2 Random Walk; 10.2.1 ID Random Walk; 10.2.2 Random Walk in Higher Dimensions; 10.3 Lévy Flights; 10.4 Markov Chain; 10.5 Markov Chain Monte Carlo; 10.5.1 Metropolis-Hastings Algorithms; 10.5.2 Random Walk; 10.6 Markov Chain and Optimisation; ExercisesPART II METAHEURISTIC ALGORITHMSAn accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insighHeuristic programmingMathematical optimizationEngineering mathematicsHeuristic programming.Mathematical optimization.Engineering mathematics.620.001/5196Yang Xin-She781375MiAaPQMiAaPQMiAaPQBOOK9910814521403321Engineering optimization4078922UNINA