pyomo。用于微分和代数方程优化的建模和自动离散化框架

IF 4.3 1区数学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Mathematical Programming Computation Pub Date : 2018-06-01 DOI:10.1007/s12532-017-0127-0

Bethany L. Nicholson, John D. Siirola, J. Watson, V. Zavala, L. Biegler

引用次数: 90

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations

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来源期刊

Mathematical Programming Computation OPERATIONS RESEARCH & MANAGEMENT SCIENCE-

CiteScore

10.80

自引率

4.80%

发文量

期刊介绍： Mathematical Programming Computation (MPC) publishes original research articles advancing the state of the art of practical computation in Mathematical Optimization and closely related fields. Authors are required to submit software source code and data along with their manuscripts (while open-source software is encouraged, it is not required). Where applicable, the review process will aim for verification of reported computational results. Topics of articles include: New algorithmic techniques, with substantial computational testing New applications, with substantial computational testing Innovative software Comparative tests of algorithms Modeling environments Libraries of problem instances Software frameworks or libraries. Among the specific topics covered in MPC are linear programming, convex optimization, nonlinear optimization, stochastic optimization, integer programming, combinatorial optimization, global optimization, network algorithms, and modeling languages. MPC accepts manuscript submission from its own editorial board members in cases in which the identities of the associate editor, reviewers, and technical editor handling the manuscript can remain fully confidential. To be accepted, manuscripts submitted by editorial board members must meet the same quality standards as all other accepted submissions; there is absolutely no special preference or consideration given to such submissions.