Remarks on: A Lagrangian dual method for two-stage robust optimization with binary uncertainties

Abstract We provide a correction to the sufficient conditions under which closed-form expressions for the optimal Lagrange multiplier are provided in Subramanyam (2022). We first present a simple counterexample where the original conditions are insufficient, highlight where the original proof fails, and then provide modified conditions along with a correct proof of their validity. Finally, although the original paper discusses modifications to their method for problems that may not satisfy any sufficient conditions, we substantiate that discussion along two directions. We first show that computing an optimal Lagrange multiplier can still be done in polynomial time. We then provide complete and correct versions of the corresponding Benders and column-and-constraint generation algorithms in which the original method is used. We also discuss the implications of our findings on computational performance.

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Publication Details

Journal
Optimization and Engineering
Published
2025-08-29
DOI
https://doi.org/10.1007/s11081-025-10015-y
Citations
1
Primary Topic
Risk and Portfolio Optimization
Type
article
Field-Weighted Citation Impact
2.11

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article

Remarks on: A Lagrangian dual method for two-stage robust optimization with binary uncertainties

Anirudh Subramanyam, Henri Lefebvre
1 citations
Optimization and Engineering
Risk and Portfolio Optimization
2.11
article

Remarks on: A Lagrangian dual method for two-stage robust optimization with binary uncertainties

Anirudh Subramanyam, Henri Lefebvre
article en
1 citations

Abstract

Abstract We provide a correction to the sufficient conditions under which closed-form expressions for the optimal Lagrange multiplier are provided in Subramanyam (2022). We first present a simple counterexample where the original conditions are insufficient, highlight where the original proof fails, and then provide modified conditions along with a correct proof of their validity. Finally, although the original paper discusses modifications to their method for problems that may not satisfy any sufficient conditions, we substantiate that discussion along two directions. We first show that computing an optimal Lagrange multiplier can still be done in polynomial time. We then provide complete and correct versions of the corresponding Benders and column-and-constraint generation algorithms in which the original method is used. We also discuss the implications of our findings on computational performance.

Optimization and EngineeringVol. 27(1)
Pennsylvania State University (US), Universität Trier (DE)
National Science Foundation, Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 11%
Risk and Portfolio Optimization
2.11
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