The Exposition of 1 John and An Exposition upon Matthew V-VII are William Tyndale's two major exegetical writings, published respectively in 1531 and 1533 in Antwerp. By this period Tyndale's English translations of the New Testament and Pentateuch had both been printed, and he was preparing a revised version of the former to be published in 1534. Among the books he produced in the interim are these verse-by-verse commentaries on St. John's first epistle and on Jesus's Sermon on the Mount. In them Tyndale characteristically alternates between fierce polemics and solemn homilies that together, as has been claimed, amount to the most complete articulation of his theological positions. This volume replaces the nineteenth-century editions on which scholars and students have long relied by providing an original-spelling text of each Exposition with notes recording substantive textual variants in all sixteenth-century editions; an introduction and extensive commentary documenting, in particular, parallels and differences between the two texts and Tyndale's other works, the works of Luther and other reform theologians, and the works of the Church Fathers and others; plus a comprehensive glossary, appendices, and indices.
This book provides a complete and comprehensive reference/guide to Pyomo (Python Optimization Modeling Objects) for both beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. The text illustrates the breadth of the modeling and analysis capabilities that are supported by the software and support of complex real-world applications. Pyomo is an open source software package for formulating and solving large-scale optimization and operations research problems. The text begins with a tutorial on simple linear and integer programming models. A detailed reference of Pyomo's modeling components is illustrated with extensive examples, including a discussion of how to load data from data sources like spreadsheets and databases. Chapters describing advanced modeling capabilities for nonlinear and stochastic optimization are also included. The Pyomo software provides familiar modeling features within Python, a powerful 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. The software supports a different modeling approach than commercial AML (Algebraic Modeling Languages) tools, and is designed for flexibility, extensibility, portability, and maintainability but also maintains the central ideas in modern AMLs.
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.
This is a collection of genealogies of the early settlers of "Old Hunterdon County," New Jersey, the majority of the histories tracing families through successive generations of the 18th and 19th centuries in what is now mostly Mercer County. Composed chiefly of a recitation of births, marriages, and deaths, the family histories number more than sixty and touch on several thousand related persons, all of whom are conveniently cited in the index.
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