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Zustand: Very Good. : Este libro es una guía práctica diseñada para aprender a utilizar Pyomo, una herramienta de modelado de optimización basada en Python. A través de ejemplos detallados y casos de uso, el texto aborda desde conceptos básicos hasta técnicas avanzadas de optimización energética y programación funcional, siendo un recurso esencial para ingenieros, científicos de datos y estudiantes interesados en la investigación operativa. EAN: 9789386242891 Tipo: Libros Categoría: Tecnología|Ciencias Título: Hands-on Pyomo Autor: Gabe Hackebeil| William Hart| Carl Laird| Jean-Paul Watson| David Woodruff Idioma: en Formato: tapa blanda.…

Zustand: New. pp. 256 Illus.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New.

Zustand: New. Idioma/Language: Inglés. ?This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. This second edition provides an expanded presentation of Pyomo?s modeling capabilities, providing a broader description of the software that will enable the user to develop and optimize models. Introductory chapters have been revised to extend tutorials; chapters that discuss advanced features now include the new functionalities added to Pyomo since the first edition including generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools.  Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language.  Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions. *** Nota: Los envíos a España peninsular, Baleares y Canarias se realizan a través de mensajería urgente. No aceptamos pedidos con destino a Ceuta y Melilla.…

Zustand: New. In English.

Zustand: New. In English.

Paperback. Zustand: Brand New. 1st edition. 243 pages. 9.25x6.25x0.75 inches. In Stock.

Zustand: New. In English.

Kartoniert / Broschiert. Zustand: New.

Kartoniert / Broschiert. Zustand: New.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume collects the accepted papers presented at the Learning and Intelligent OptimizatioN conference (LION 2007 II) held December 8-12, 2007, in Trento, Italy. The motivation for the meeting is related to the current explosion in the number and variety of heuristic algorithms for hard optimization problems, which raises - merous interesting and challenging issues. Practitioners are confronted with the b- den of selecting the most appropriate method, in many cases through an expensive algorithm configuration and parameter-tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental meth- ology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the expe- menter, who, in too many cases, is 'in the loop' as a crucial intelligent learning c- ponent. Both issues are related to designing and engineering ways of 'learning' about the performance of different techniques, and ways of using memory about algorithm behavior in the past to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained from different runs or during a single run can - prove the algorithm development and design process and simplify the applications of high-performance optimization methods. Combinations of algorithms can further improve the robustness and performance of the individual components provided that sufficient knowledge of the relationship between problem instance characteristics and algorithm performance is obtained.…

Taschenbuch. Zustand: Neu. Learning and Intelligent Optimization | Second International Conference, LION 2007 II, Trento, Italy, December 8-12, 2007. Selected Papers | Vittorio Maniezzo (u. a.) | Taschenbuch | xii | Englisch | 2008 | Springer | EAN 9783540926948 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 256 | Sprache: Englisch | Produktart: Bücher | This volume collects the accepted papers presented at the Learning and Intelligent OptimizatioN conference (LION 2007 II) held December 8¿12, 2007, in Trento, Italy. The motivation for the meeting is related to the current explosion in the number and variety of heuristic algorithms for hard optimization problems, which raises - merous interesting and challenging issues. Practitioners are confronted with the b- den of selecting the most appropriate method, in many cases through an expensive algorithm configuration and parameter-tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental meth- ology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the expe- menter, who, in too many cases, is "in the loop" as a crucial intelligent learning c- ponent. Both issues are related to designing and engineering ways of "learning" about the performance of different techniques, and ways of using memory about algorithm behavior in the past to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained from different runs or during a single run can - prove the algorithm development and design process and simplify the applications of high-performance optimization methods. Combinations of algorithms can further improve the robustness and performance of the individual components provided that sufficient knowledge of the relationship between problem instance characteristics and algorithm performance is obtained.…

Hardcover. Zustand: Brand New. 3rd edition. 242 pages. 9.25x6.10x0.71 inches. In Stock.

Hardcover. Zustand: Very Good. 1st Edition. In French. Broché, 37x27cm, 97p. Avec plusieurs photos. [1800].