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An extension of the entropic perturbation method of linear programming.

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Title: An extension of the entropic perturbation method of linear programming.
Authors: Tian, D. G., Fei, Q.
Source: Mathematical Methods of Operations Research. 1999, Vol. 50 Issue 1, p17. 9p.
Subjects: Entropy, Perturbation theory, Linear programming
Abstract: Abstract. In this paper, an extended form of the entropic perturbation method of linear programming is given, which can overcome the weakness of the original method -- being easy of overflow in computing. Moreover, the global convergence of the gradient algorithm for the method is discussed. [ABSTRACT FROM AUTHOR]
Copyright of Mathematical Methods of Operations Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: An extension of the entropic perturbation method of linear programming.
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  Data: Abstract. In this paper, an extended form of the entropic perturbation method of linear programming is given, which can overcome the weakness of the original method -- being easy of overflow in computing. Moreover, the global convergence of the gradient algorithm for the method is discussed. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Mathematical Methods of Operations Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/pl00020924
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      – SubjectFull: Linear programming
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              Text: 1999
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