一种用于凸优化的python嵌入式建模语言。

IF 4.3 3区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Journal of Machine Learning Research Pub Date : 2016-04-01

Steven Diamond, Stephen Boyd

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引用次数: 0

摘要

CVXPY是一种特定于领域的语言，用于在Python中嵌入凸优化。它允许用户用遵循数学的自然语法来表达凸优化问题，而不是用求解器所要求的限制性标准形式。CVXPY可以很容易地将凸优化与Python的高级特性(如并行性和面向对象设计)结合起来。CVXPY在GPL许可下可在http://www.cvxpy.org/获得，以及文档和示例。

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本刊更多论文

CVXPY: A Python-Embedded Modeling Language for Convex Optimization.

CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY makes it easy to combine convex optimization with high-level features of Python such as parallelism and object-oriented design. CVXPY is available at http://www.cvxpy.org/ under the GPL license, along with documentation and examples.

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来源期刊

Journal of Machine Learning Research 工程技术-计算机：人工智能

CiteScore

18.80

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.