INFORMS Journal on Optimization

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A Semidefinite Programming Relaxation for the Sparse Integer Least Squares Problem
- Alberto Del Pia
  Alberto Del Pia
  [email protected]
  https://orcid.org/0000-0001-8428-3914
  Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, Wisconsin 53706; and Wisconsin Institute for Discovery, University of Wisconsin–Madison, Madison, Wisconsin 53706
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- Dekun Zhou
  Corresponding Author
  Dekun Zhou
  [email protected]
  https://orcid.org/0000-0003-2311-1443
  Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, Wisconsin 53706; and Wisconsin Institute for Discovery, University of Wisconsin–Madison, Madison, Wisconsin 53706
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Published Online:September 11, 2025
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Abstract
In this paper, we study the sparse integer least squares (SILS) problem, an NP-hard variant of least squares with sparse {0,±1}-vectors. We propose an ℓ1-based semidefinite programming (SDP) relaxation and propose a randomized algorithm for SILS, which computes ...
Wasserstein Distributionally Robust Shallow Convex Neural Networks
- Julien Pallage
  Corresponding Author
  Julien Pallage
  [email protected]
  https://orcid.org/0009-0001-1689-3021
  Department of Electrical Engineering, Polytechnique Montréal, Montréal, Québec H3T 0A3, Canada; and GERAD, Montréal, Québec H3T 2A7, Canada; and Mila, Montréal, Québec H2S 3H1, Canada
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- Antoine Lesage-Landry
  Antoine Lesage-Landry
  [email protected]
  https://orcid.org/0000-0001-9652-6557
  Department of Electrical Engineering, Polytechnique Montréal, Montréal, Québec H3T 0A3, Canada; and GERAD, Montréal, Québec H3T 2A7, Canada; and Mila, Montréal, Québec H2S 3H1, Canada
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Published Online:August 26, 2025
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Abstract
In this work, we propose Wasserstein distributionally robust shallow convex neural networks (WaDiRo-SCNNs) to provide reliable nonlinear predictions when subject to adverse and corrupted data sets. Our approach is based on the reformulation of a new ...
On the Out-of-Sample Performance of Stochastic Dynamic Programming and Model Predictive Control
- Dominic S. T. Keehan
  Corresponding Author
  Dominic S. T. Keehan
  [email protected]
  https://orcid.org/0000-0003-3375-3562
  University of Auckland, Auckland 1010, New Zealand
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- Andrew B. Philpott
  Andrew B. Philpott
  [email protected]
  https://orcid.org/0000-0002-7094-3405
  University of Auckland, Auckland 1010, New Zealand
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- Edward J. Anderson
  Edward J. Anderson
  [email protected]
  https://orcid.org/0000-0002-6375-4067
  Imperial College London, London SW7 2AZ, United Kingdom
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Published Online:August 1, 2025
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Abstract
This paper aims to understand when stochastic dynamic programming or model predictive control is the more appropriate method for multistage decision making under uncertainty. It reveals a connection to distributionally ambiguous optimization that depends ...
Stochastic Compositional Optimization with Compositional Constraints
- Shuoguang Yang
  Corresponding Author
  Shuoguang Yang
  [email protected]
  https://orcid.org/0000-0002-0915-196X
  Department of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China
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- Wei You
  Wei You
  [email protected]
  https://orcid.org/0000-0003-0844-4194
  Department of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China
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- Zhe Zhang
  Zhe Zhang
  [email protected]
  https://orcid.org/0009-0008-0204-0571
  School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47907
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- Ethan X. Fang
  Ethan X. Fang
  [email protected]
  https://orcid.org/0000-0003-3762-9155
  Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27705
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Published Online:June 12, 2025
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Abstract
Stochastic compositional optimization (SCO) has attracted considerable attention because of its broad applicability to important real-world problems. However, existing work on SCO typically assumes that the projection within a solution update is ...
On the Tradeoff Between Distributional Belief and Ambiguity: Conservatism, Finite-Sample Guarantees, and Asymptotic Properties
- Man Yiu Tsang
  Man Yiu Tsang
  [email protected]
  https://orcid.org/0000-0002-0965-118X
  Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015
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- Karmel S. Shehadeh
  Corresponding Author
  Karmel S. Shehadeh
  [email protected]
  https://orcid.org/0000-0001-7842-0951
  Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California 90089
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Published Online:April 24, 2025
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Abstract
We propose and analyze a new data-driven tradeoff (TRO) approach for modeling uncertainty that serves as a middle ground between the optimistic approach, which adopts a distributional belief, and the pessimistic distributionally robust optimization ...

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