Academic Journal
Efficient Algorithms for a Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management.
| Title: | Efficient Algorithms for a Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management. |
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| Authors: | Chen, Xin1 (AUTHOR) xinchen@gatech.edu, He, Niao2 (AUTHOR) niao.he@inf.ethz.ch, Hu, Yifan2 (AUTHOR) yifan.hu@epfl.ch, Ye, Zikun3 (AUTHOR) zikunye@uw.edu |
| Source: | Operations Research. Mar/Apr2025, Vol. 73 Issue 2, p704-719. 16p. |
| Subject Terms: | *Revenue management, *Computer programming, *Data analytics, Convex functions, Global optimization |
| Abstract: | Stochastic Hidden Convex Optimization and Its Applications How to solve nonconvex optimization to global optimality is challenging and important for various applications. In "Efficient Algorithms for a Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management," Chen, He, Hu, and Ye designed three algorithms that converge to global optimality efficiently for a class of stochastic nonconvex optimization that admits implicit hidden convexity (there exists an inaccessible convex reformulation). In particular, the complexity of the proposed mirror stochastic gradient (MSG) method matches the optimal complexity of black-box first-order methods for stochastic convex optimization. The authors applied the proposed MSG algorithm to solve both passenger and air-cargo network revenue management problems considering the booking limit control policy. The extensive numerical experiments demonstrate the superior performance of MSG algorithm for booking limit control with higher revenue and lower computation cost than state-of-the-art bid-price-based control policies, especially when the variance of random capacity is large. We study a class of stochastic nonconvex optimization in the form of minx∈XF(x)≔Eξ[f(ϕ(x,ξ))] , that is, F is a composition of a convex function f and a random function ϕ. Leveraging an (implicit) convex reformulation via a variable transformation u=E[ϕ(x,ξ)] , we develop stochastic gradient-based algorithms and establish their sample and gradient complexities for achieving an ϵ -global optimal solution. Interestingly, our proposed Mirror Stochastic Gradient (MSG) method operates only in the original x-space using gradient estimators of the original nonconvex objective F and achieves O˜(ϵ−2) complexities, matching the lower bounds for solving stochastic convex optimization problems. Under booking limits control, we formulate the air-cargo network revenue management (NRM) problem with random two-dimensional capacity, random consumption, and routing flexibility as a special case of the stochastic nonconvex optimization, where the random function ϕ(x,ξ)=x∧ξ , that is, the random demand ξ truncates the booking limit decision x. Extensive numerical experiments demonstrate the superior performance of our proposed MSG algorithm for booking limit control with higher revenue and lower computation cost than state-of-the-art bid-price-based control policies, especially when the variance of random capacity is large. Funding: This work was partly supported by the National Science Foundation [Grants CMMI-1761699, CRII-1755829], the ZJU-UIUC Institute Research Program, and NCCR Automation in Switzerland. Supplemental Material: The computer code and data that support the findings of this study are available within this article's supplemental material at https://doi.org/10.1287/opre.2022.0216. [ABSTRACT FROM AUTHOR] |
| Copyright of Operations Research is the property of INFORMS: Institute for Operations Research & the Management Sciences 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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| Items | – Name: Title Label: Title Group: Ti Data: Efficient Algorithms for a Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Xin%22">Chen, Xin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xinchen@gatech.edu</i><br /><searchLink fieldCode="AR" term="%22He%2C+Niao%22">He, Niao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> niao.he@inf.ethz.ch</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Yifan%22">Hu, Yifan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> yifan.hu@epfl.ch</i><br /><searchLink fieldCode="AR" term="%22Ye%2C+Zikun%22">Ye, Zikun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> zikunye@uw.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Operations+Research%22">Operations Research</searchLink>. Mar/Apr2025, Vol. 73 Issue 2, p704-719. 16p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Revenue+management%22">Revenue management</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Convex+functions%22">Convex functions</searchLink><br /><searchLink fieldCode="DE" term="%22Global+optimization%22">Global optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Stochastic Hidden Convex Optimization and Its Applications How to solve nonconvex optimization to global optimality is challenging and important for various applications. In "Efficient Algorithms for a Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management," Chen, He, Hu, and Ye designed three algorithms that converge to global optimality efficiently for a class of stochastic nonconvex optimization that admits implicit hidden convexity (there exists an inaccessible convex reformulation). In particular, the complexity of the proposed mirror stochastic gradient (MSG) method matches the optimal complexity of black-box first-order methods for stochastic convex optimization. The authors applied the proposed MSG algorithm to solve both passenger and air-cargo network revenue management problems considering the booking limit control policy. The extensive numerical experiments demonstrate the superior performance of MSG algorithm for booking limit control with higher revenue and lower computation cost than state-of-the-art bid-price-based control policies, especially when the variance of random capacity is large. We study a class of stochastic nonconvex optimization in the form of minx∈XF(x)≔Eξ[f(ϕ(x,ξ))] , that is, F is a composition of a convex function f and a random function ϕ. Leveraging an (implicit) convex reformulation via a variable transformation u=E[ϕ(x,ξ)] , we develop stochastic gradient-based algorithms and establish their sample and gradient complexities for achieving an ϵ -global optimal solution. Interestingly, our proposed Mirror Stochastic Gradient (MSG) method operates only in the original x-space using gradient estimators of the original nonconvex objective F and achieves O˜(ϵ−2) complexities, matching the lower bounds for solving stochastic convex optimization problems. Under booking limits control, we formulate the air-cargo network revenue management (NRM) problem with random two-dimensional capacity, random consumption, and routing flexibility as a special case of the stochastic nonconvex optimization, where the random function ϕ(x,ξ)=x∧ξ , that is, the random demand ξ truncates the booking limit decision x. Extensive numerical experiments demonstrate the superior performance of our proposed MSG algorithm for booking limit control with higher revenue and lower computation cost than state-of-the-art bid-price-based control policies, especially when the variance of random capacity is large. Funding: This work was partly supported by the National Science Foundation [Grants CMMI-1761699, CRII-1755829], the ZJU-UIUC Institute Research Program, and NCCR Automation in Switzerland. Supplemental Material: The computer code and data that support the findings of this study are available within this article's supplemental material at https://doi.org/10.1287/opre.2022.0216. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Operations Research is the property of INFORMS: Institute for Operations Research & the Management Sciences 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1287/opre.2022.0216 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 704 Subjects: – SubjectFull: Revenue management Type: general – SubjectFull: Computer programming Type: general – SubjectFull: Data analytics Type: general – SubjectFull: Convex functions Type: general – SubjectFull: Global optimization Type: general Titles: – TitleFull: Efficient Algorithms for a Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Xin – PersonEntity: Name: NameFull: He, Niao – PersonEntity: Name: NameFull: Hu, Yifan – PersonEntity: Name: NameFull: Ye, Zikun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar/Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 0030364X Numbering: – Type: volume Value: 73 – Type: issue Value: 2 Titles: – TitleFull: Operations Research Type: main |
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