INFORMS Journal on Optimization

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On the Power of Linear Programming for K-Means Clustering
- Antonio De Rosa
  Antonio De Rosa
  [email protected]
  Department of Decision Sciences, Bocconi University, 20136 Milano, Italy; and The Bocconi Institute for Data Science and Analytics (BIDSA), Bocconi University, 20136 Milano, Italy
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- Aida Khajavirad
  Corresponding Author
  Aida Khajavirad
  [email protected]
  https://orcid.org/0000-0002-7097-1676
  Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015
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- Yakun Wang
  Yakun Wang
  [email protected]
  https://orcid.org/0009-0009-4995-0131
  Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015
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Published Online:May 19, 2026
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Abstract
De Rosa and Khajavirad [De Rosa A, Khajavirad A (2022) The ratio-cut polytope and K-means clustering. SIAM J. Optim. 32(1):173–203] introduced a new polynomial-size linear programming (LP) relaxation for K-means clustering. In this paper, we further ...
Mixed-Integer Programming for a Class of Robust Submodular Maximization Problems
- Hsin-Yi Huang
  Hsin-Yi Huang
  [email protected]
  Department of Management Science, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
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- Hao-Hsiang Wu
  Hao-Hsiang Wu
  [email protected]
  https://orcid.org/0000-0001-9665-8672
  Department of Management Science, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
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- Simge Küçükyavuz
  Corresponding Author
  Simge Küçükyavuz
  [email protected]
  https://orcid.org/0000-0001-6548-9378
  Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208
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Published Online:May 15, 2026
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Abstract
We consider robust submodular maximization problems (RSMs), where given a set of m monotone submodular objective functions, the robustness is with respect to the worst-case (scaled) objective function. The model we consider generalizes two variants of ...
Sketching Stochastic Valuation Functions
- Milan Vojnović
  Milan Vojnović
  [email protected]
  https://orcid.org/0000-0003-1382-022X
  Department of Statistics, London School of Economics, London WC2A 2AE, United Kingdom
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- Yiliu Wang
  Corresponding Author
  Yiliu Wang
  [email protected]
  https://orcid.org/0009-0006-9536-8482
  Allen Institute, University of Washington, Seattle, Washington 98195; and Department of Neurobiology & Biophysics, University of Washington, Seattle, Washington 98195
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Published Online:April 6, 2026
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Abstract
We consider the problem of sketching set valuation functions, defined as the expectation of a valuation function applied to independent random item values. For valuation functions that are monotone and either subadditive or submodular and that satisfy a ...
Partial Identification with Proxy of Latent Confoundings via Sum-of-Ratios Fractional Programming
- Zhiheng Zhang
  Zhiheng Zhang
  [email protected]
  https://orcid.org/0000-0001-6767-7487
  The School of Statistics and Data Science, Shanghai University of Finance and Economics, Shanghai 200433, P.R. China; and Institute of Data Science and Statistics, Shanghai University of Finance and Economics, Shanghai 200433, P.R. China; and Institute of Big Data Research, Shanghai University of Finance and Economics, Shanghai 200433, P.R. China
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Published Online:March 26, 2026
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Abstract
Unobserved confounding poses a central challenge to the credible estimation of causal effects. Proxy-based negative-control methods address this by using auxiliary outcome variables W as proxies for latent confounders U, but they typically rely on strong ...
A Randomized Block-Coordinate Primal-Dual Method for Large-Scale Stochastic Saddle Point Problems
- Erfan Yazdandoost Hamedani
  Erfan Yazdandoost Hamedani
  [email protected]
  https://orcid.org/0000-0002-3229-3499
  Systems and Industrial Engineering Department, The University of Arizona, Tucson, Arizona 85721
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- Afrooz Jalilzadeh
  Afrooz Jalilzadeh
  [email protected]
  https://orcid.org/0000-0002-3734-1082
  Systems and Industrial Engineering Department, The University of Arizona, Tucson, Arizona 85721
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- Necdet Serhat Aybat
  Corresponding Author
  Necdet Serhat Aybat
  [email protected]
  https://orcid.org/0000-0002-9839-9894
  Industrial and Manufacturing Engineering Department, The Pennsylvania State University, University Park, Pennsylvania 16802
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Published Online:February 25, 2026
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Abstract
We consider (stochastic) convex-concave saddle point (SP) problems with high-dimensional decision variables, arising in various applications including machine learning problems. To contend with the challenges in computing full gradients, we employ a ...

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