Pyomo -- Optimization Modeling in Python
暫譯: Pyomo -- 在 Python 中的最佳化建模

Bynum, Michael L., Hackebeil, Gabriel A., Hart, William E.

  • 出版商: Springer
  • 出版日期: 2021-03-31
  • 售價: $2,960
  • 貴賓價: 9.5$2,812
  • 語言: 英文
  • 頁數: 225
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3030689271
  • ISBN-13: 9783030689278
  • 相關分類: Python程式語言
  • 海外代購書籍(需單獨結帳)

商品描述

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. In the third edition, much of the material has been reorganized, new examples have been added, and a new chapter has been added describing how modelers can improve the performance of their models. The authors have also modified their recommended method for importing Pyomo. A big change in this edition is the emphasis of concrete models, which provide fewer restrictions on the specification and use of Pyomo models.

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.

商品描述(中文翻譯)

這本書為初學者和進階模型設計者提供了完整且全面的 Pyomo(Python 優化建模物件)指南,包括本科生和研究生、學術研究人員以及實務工作者。書中使用許多範例來說明不同的技術,這些技術對於模型的制定非常有用,並且清楚地闡明了 Pyomo 所支持的建模能力的廣度及其處理複雜現實應用的能力。在第三版中,許多內容已重新組織,新增了範例,並增加了一章描述模型設計者如何改善其模型性能。作者還修改了他們推薦的 Pyomo 匯入方法。本版的一個重大變化是強調具體模型,這些模型對於 Pyomo 模型的規範和使用提供了更少的限制。

Pyomo 是一個開源軟體包,用於制定和解決大規模優化問題。該軟體擴展了現代 AML(代數建模語言)工具所支持的建模方法。Pyomo 是一個靈活、可擴展且可攜帶的 AML,嵌入於 Python 中,Python 是一種功能齊全的腳本語言。Python 是一種強大且動態的程式語言,具有非常清晰、可讀的語法和直觀的物件導向特性。Pyomo 包含用於定義稀疏集合、參數和變數的 Python 類,這些可以用來制定定義目標和約束的代數表達式。此外,Pyomo 可以從命令行介面和 Python 的互動命令環境中使用,這使得創建 Pyomo 模型、應用各種優化器和檢查解決方案變得容易。

作者簡介

William E. Hart, Carl D. Laird, Bethany L. Nicholson, John D. Siirola, and Michael L. Bynum are researchers affiliated with the Sandia National Laboratories in Albuquerque, New Mexico. Jean-Paul Watson is a researcher with the Lawrence Livermore Laboratory. David L. Woodruff is professor at the graduate school of management at the University of California, Davis. Gabriel Hackebeil is affiliated with Deepfield Nokia, Ann Arbor, MI. The 2019 INFORMS Computing Society prize was awarded to William E. Hart, Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Gabriel A. Hackebeil, Bethany L. Nicholson and John Siirola for spearheading the creation and advancement of Pyomo, an open-source software package for modeling and solving mathematical programs in Python.

作者簡介(中文翻譯)

威廉·E·哈特(William E. Hart)、卡爾·D·萊爾德(Carl D. Laird)、貝瑟妮·L·尼科爾森(Bethany L. Nicholson)、約翰·D·西羅拉(John D. Siirola)和邁克爾·L·拜納姆(Michael L. Bynum)是美國新墨西哥州阿爾伯克基的桑迪亞國家實驗室(Sandia National Laboratories)所屬的研究人員。讓-保羅·沃森(Jean-Paul Watson)是洛倫斯利物浦國家實驗室(Lawrence Livermore Laboratory)的研究人員。大衛·L·伍德拉夫(David L. Woodruff)是加州大學戴維斯分校(University of California, Davis)管理研究所的教授。加布里埃爾·哈克比爾(Gabriel Hackebeil)隸屬於位於密西根州安娜堡的Deepfield Nokia。2019年INFORMS計算社會獎頒發給威廉·E·哈特、卡爾·D·萊爾德、讓-保羅·沃森、大衛·L·伍德拉夫、加布里埃爾·A·哈克比爾、貝瑟妮·L·尼科爾森和約翰·西羅拉,以表彰他們在推動Pyomo的創建和發展方面的貢獻,Pyomo是一個用於在Python中建模和解決數學程序的開源軟體包。

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