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Libri sul tema "Optimization algorithms"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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1

Spedicato, Emilio, ed. Algorithms for Continuous Optimization. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-009-0369-2.

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2

Cheng, Shi, and Yuhui Shi, eds. Brain Storm Optimization Algorithms. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15070-9.

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3

L, Kreher Donald, ed. Graphs, algorithms, and optimization. Boca Raton: Chapman & Hall/CRC, 2005.

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4

1939-, Dennis J. E., and Institute for Computer Applications in Science and Engineering., eds. Algorithms for bilevel optimization. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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5

Heiko, Rieger, ed. Optimization algorithms in physics. Berlin: Wiley-VCH, 2002.

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6

Pereira, Ana I., Florbela P. Fernandes, João P. Coelho, João P. Teixeira, Maria F. Pacheco, Paulo Alves, and Rui P. Lopes, eds. Optimization, Learning Algorithms and Applications. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91885-9.

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7

Grötschel, Martin, László Lovász, and Alexander Schrijver. Geometric Algorithms and Combinatorial Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78240-4.

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8

Uryasev, Stanislav, and Panos M. Pardalos, eds. Stochastic Optimization: Algorithms and Applications. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-6594-6.

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9

Jansen, Klaus, and José Rolim, eds. Approximation Algorithms for Combinatiorial Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0053958.

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10

Migdalas, Athanasios, Panos M. Pardalos, and Peter Värbrand, eds. Multilevel Optimization: Algorithms and Applications. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4613-0307-7.

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11

Fukunaga, Takuro, and Ken-ichi Kawarabayashi, eds. Combinatorial Optimization and Graph Algorithms. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6147-9.

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12

Koziel, Slawomir, and Xin-She Yang, eds. Computational Optimization, Methods and Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20859-1.

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13

Jansen, Klaus, Stefano Leonardi, and Vijay Vazirani, eds. Approximation Algorithms for Combinatorial Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45753-4.

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14

Bhatnagar, S., H. L. Prasad, and L. A. Prashanth. Stochastic Recursive Algorithms for Optimization. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4285-0.

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15

Grötschel, Martin, László Lovász, and Alexander Schrijver. Geometric Algorithms and Combinatorial Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-97881-4.

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16

Bahmani, Sohail. Algorithms for Sparsity-Constrained Optimization. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01881-2.

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17

Jansen, Klaus, and Samir Khuller, eds. Approximation Algorithms for Combinatorial Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44436-x.

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18

Polak, E. Optimization: Algorithms and consistent approximations. New York: Springer-Verlag, 1997.

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19

Pereira, Ana I., Andrej Košir, Florbela P. Fernandes, Maria F. Pacheco, João P. Teixeira, and Rui P. Lopes, eds. Optimization, Learning Algorithms and Applications. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-23236-7.

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20

James, Evans. Optimization Algorithms for Networks and Graphs. Taylor & Francis Group, 2017.

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21

James, Evans. Optimization Algorithms for Networks and Graphs. Taylor & Francis Group, 2017.

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22

Optimization Algorithms for Networks and Graphs. Taylor & Francis Group, 2017.

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23

Badar, Altaf Q. H. Evolutionary Optimization Algorithms. Taylor & Francis Group, 2021.

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24

Kochenderfer, Mykel J., and Tim A. Wheeler. Algorithms for Optimization. MIT Press, 2019.

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25

Simon, Dan. Evolutionary Optimization Algorithms. Wiley & Sons, Incorporated, John, 2013.

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26

Lange, Kenneth. MM Optimization Algorithms. SIAM-Society for Industrial and Applied Mathematics, 2016.

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27

Valdman, Jan, ed. Optimization Algorithms - Examples. InTech, 2018. http://dx.doi.org/10.5772/intechopen.71370.

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28

Kochenderfer, Mykel J., and Tim A. Wheeler. Algorithms for Optimization. The MIT Press, 2019.

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29

Natureinspired Optimization Algorithms. Elsevier Science Publishing Co Inc, 2014.

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30

Evolutionary Optimization Algorithms. Taylor & Francis Group, 2021.

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31

Simon, Dan. Evolutionary Optimization Algorithms. Wiley & Sons, Incorporated, John, 2014.

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32

Simon, Dan. Evolutionary Optimization Algorithms. Wiley & Sons, Incorporated, John, 2014.

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33

Badar, Altaf Q. H. Evolutionary Optimization Algorithms. Taylor & Francis Group, 2021.

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34

Kochenderfer, Mykel J., and Tim A. Wheeler. Algorithms for Optimization. MIT Press, 2019.

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35

Bertsekas, Dimitri P. Convex Optimization Algorithms. Athena Scientific, 2015.

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36

Badar, Altaf Q. H. Evolutionary Optimization Algorithms. Taylor & Francis Group, 2021.

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37

Simon, Dan. Evolutionary Optimization Algorithms. Wiley & Sons, Incorporated, John, 2013.

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38

Valdman, Jan. Optimization Algorithms: Examples. IntechOpen, 2018.

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39

Evolutionary Optimization Algorithms. Wiley & Sons, Incorporated, John, 2013.

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40

Kreher, Donald L., and William Kocay. Graphs, Algorithms, and Optimization. Taylor & Francis Group, 2016.

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41

Optimization Algorithms [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.94617.

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42

Michel, Bierlaire. Optimization: Principles and Algorithms. EPFL Press, 2015. http://dx.doi.org/10.55430/6116v1mb.

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Abstract (sommario):

Every engineer and decision scientist must have a good mastery of optimization, an essential element in their toolkit. Thus, this articulate introductory textbook will certainly be welcomed by students and practicing professionals alike. Drawing from his vast teaching experience, the author skillfully leads the reader through a rich choice of topics in a coherent, fluid and tasteful blend of models and methods anchored on the underlying mathematical notions (only prerequisites: first year calculus and linear algebra). Topics range from the classics to some of the most recent developments in smooth unconstrained and constrained optimization, like descent methods, conjugate gradients, Newton and quasi-Newton methods, linear programming and the simplex method, trust region and interior point methods. Furthermore elements of discrete and combinatorial optimization like network optimization, integer programming and heuristic local search methods are also presented. This book presents optimization as a modeling tool that beyond supporting problem formulation plus design and implementation of efficient algorithms, also is a language suited for interdisciplinary human interaction. Readers further become aware that while the roots of mathematical optimization go back to the work of giants like Newton, Lagrange, Cauchy, Euler or Gauss, it did not become a discipline on its own until World War Two. Also that its present momentum really resulted from its symbiosis with modern computers, which made it possible to routinely solve problems with millions of variables and constraints. With his witty, entertaining, yet precise style, Michel Bierlaire captivates his readers and awakens their desire to try out the presented material in a creative mode. One of the outstanding assets of this book is the unified, clear and concise rendering of the various algorithms, which makes them easily readable and translatable into any high level programming language. ''This is an addictive book that I am very pleased to recommend.'' Prof. Thomas M. Liebling

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43

Nature-Inspired Optimization Algorithms. Elsevier, 2014. http://dx.doi.org/10.1016/c2013-0-01368-0.

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44

Kocay, William, and Donald L. Kreher. Graphs, Algorithms, and Optimization. Edited by Kenneth H. Rosen. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315272689.

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45

Nature-Inspired Optimization Algorithms. Elsevier, 2021. http://dx.doi.org/10.1016/c2019-0-03762-4.

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46

Khamparia, Aditya, Ashish Khanna, Nhu Gia Nguyen, and Bao Le Nguyen, eds. Nature-Inspired Optimization Algorithms. De Gruyter, 2021. http://dx.doi.org/10.1515/9783110676112.

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47

Graphs, Algorithms, and Optimization. Routledge, 2004. http://dx.doi.org/10.4324/9780203489055.

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48

A, Vasuki. Nature-Inspired Optimization Algorithms. Taylor & Francis Group, 2020.

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49

Kreher, Donald L., and William Kocay. Graphs, Algorithms, and Optimization. Taylor & Francis Group, 2004.

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50

Nature-Inspired Optimization Algorithms. Elsevier, 2016.

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