Analysis of Subdifferentials of Marginal and Performance Functions: Analysis of Subdifferentials of Marginal and Performance...: D.T.V. An, J.-P. Penot.

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Title: Analysis of Subdifferentials of Marginal and Performance Functions: Analysis of Subdifferentials of Marginal and Performance...: D.T.V. An, J.-P. Penot.
Authors: An, Duong Thi Viet1 (AUTHOR) andtv@tnus.edu.vn, Penot, Jean-Paul2 (AUTHOR) jean-paul.penot@sorbonne-universite.fr
Source: Applied Mathematics & Optimization. Jun2025, Vol. 91 Issue 3, p1-28. 28p.
Subjects: Subdifferentials
Abstract: We study generalized derivatives of value functions for optimization problems depending on a parameter w. Interpretations of the results obtained with these substitutes to derivatives are known to be important. We endeavour to answer the question: can one obtain these results without knowing the nature of these substitutes and their constructions? Is there a means to obtain them in a unified way? [ABSTRACT FROM AUTHOR]
Copyright of Applied Mathematics & Optimization 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: We study generalized derivatives of value functions for optimization problems depending on a parameter w. Interpretations of the results obtained with these substitutes to derivatives are known to be important. We endeavour to answer the question: can one obtain these results without knowing the nature of these substitutes and their constructions? Is there a means to obtain them in a unified way? [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Applied Mathematics & Optimization 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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