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1. |
Record Nr. |
UNISA990000707440203316 |
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Autore |
ARDEMANI, Edoardo |
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Titolo |
L' economia delle imprese |
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Pubbl/distr/stampa |
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Descrizione fisica |
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Disciplina |
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Soggetti |
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Collocazione |
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658.1 ARD 3/1 (IEP VI 159/I) |
658.1 ARD 3/1a (IEP VI 159/I BIS) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910814521403321 |
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Autore |
Yang Xin-She |
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Titolo |
Engineering optimization : an introduction with metaheuristic applications / / Xin-She Yang |
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Pubbl/distr/stampa |
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Hoboken, NJ, : Wiley, c2010 |
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ISBN |
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9786612707773 |
9781282707771 |
1282707779 |
9780470640425 |
0470640421 |
9780470640418 |
0470640413 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (377 p.) |
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Disciplina |
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Soggetti |
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Heuristic programming |
Mathematical optimization |
Engineering mathematics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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 Calculus |
3.3 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 Optimization |
4.2 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 Programming |
5.4.2 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; Exercises |
8 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; Exercises |
PART II METAHEURISTIC ALGORITHMS |
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Sommario/riassunto |
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An 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 |
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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 insigh |
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3. |
Record Nr. |
UNINA9911051729603321 |
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Autore |
Baracca, Angelo |
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Titolo |
Scienza e industria 1848-1915 : gli sviluppi scientifici connessi alla seconda rivoluzione industriale / Angelo Baracca, Stefano Ruffo, Arturo Russo |
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Pubbl/distr/stampa |
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Roma ; Bari, : Edizioni Laterza, 1979 |
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Descrizione fisica |
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XII, 354 p. : ill. ; 18 cm |
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Collana |
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Altri autori (Persone) |
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Russo, Arturo |
Ruffo, Stefano |
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Disciplina |
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Locazione |
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Collocazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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