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arXiv math.OC Optimization and Control
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arXiv math.OC Optimization and Control
@mathOCb
Unofficial bot by @vela with github.com/so-okada/twXiv. @mathMPb @mathNAb @mathNTb @mathOAb @mathPRb @mathQAb @mathRAb @mathRTb @mathSGb @mathSPb ...
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    arXiv math.OC Optimization and Control
    @mathOCb
    Nov 6, 2023
    Dimitris Bertsimas, Georgios Margaritis: Global Optimization: A Machine Learning Approach arxiv.org/abs/2311.01742 arxiv.org/pdf/2311.01742
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    Global Optimization: A Machine Learning Approach
    Many approaches for addressing Global Optimization problems typically rely on relaxations of nonlinear constraints over specific mathematical primitives. This is restricting in applications with...
    6.4K
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    arXiv math.OC Optimization and Control
    @mathOCb
    Jun 18, 2024
    V. S. Mikhalevich, A. M. Gupal, V. I. Norkin: Methods of Nonconvex Optimization arxiv.org/abs/2406.10406 arxiv.org/pdf/2406.10406
    5.1K
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    arXiv math.OC Optimization and Control
    @mathOCb
    Nov 6, 2024
    Daniel Kuhn, Soroosh Shafiee, Wolfram Wiesemann: Distributionally Robust Optimization arxiv.org/abs/2411.02549 arxiv.org/pdf/2411.02549
    6.8K
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    arXiv math.OC Optimization and Control
    @mathOCb
    May 2, 2024
    Maxim Raginsky: A variational approach to sampling in diffusion processes arxiv.org/abs/2405.00126 arxiv.org/pdf/2405.00126
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    A variational approach to sampling in diffusion processes
    We revisit the work of Mitter and Newton on an information-theoretic interpretation of Bayes' formula through the Gibbs variational principle. This formulation allowed them to pose nonlinear...
    1.9K
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    arXiv math.OC Optimization and Control
    @mathOCb
    Dec 18, 2023
    Veronica Piccialli, Jan Schwiddessen, Antonio M. Sudoso: Optimization meets Machine Learning: An Exact Algorithm for Semi-Supervised Support Vector Machines arxiv.org/abs/2312.09789 arxiv.org/pdf/2312.09789
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    Optimization meets Machine Learning: An Exact Algorithm for...
    Support vector machines (SVMs) are well-studied supervised learning models for binary classification. In many applications, large amounts of samples can be cheaply and easily obtained. What is...
    2.3K
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    arXiv math.OC Optimization and Control
    @mathOCb
    Jan 22, 2021
    Simge Küçükyavuz, Ruiwei Jiang: Chance-Constrained Optimization: A Review of Mixed-Integer Conic Formulations and Applications arxiv.org/abs/2101.08746 arxiv.org/pdf/2101.08746
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    arXiv math.OC Optimization and Control
    @mathOCb
    Nov 30, 2023
    Jaeyeon Kim, Chanwoo Park, Asuman Ozdaglar, Jelena Diakonikolas, Ernest K. Ryu: Mirror Duality in Convex Optimization arxiv.org/abs/2311.17296 arxiv.org/pdf/2311.17296
    4.9K
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    arXiv math.OC Optimization and Control
    @mathOCb
    Aug 14, 2025
    Bahar Taskesen: Optimal Transport on Lie Group Orbits arxiv.org/abs/2508.09377 arxiv.org/pdf/2508.09377 arxiv.org/html/2508.09377
    7.5K
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    arXiv math.OC Optimization and Control
    @mathOCb
    Apr 25, 2025
    Eric Schmid: Applied Sheaf Theory For Multi-agent Artificial Intelligence (... arxiv.org/abs/2504.17700 Mirko Fiacchini, et al.: Recursive feasibility for stochastic MPC and the rationale beh... arxiv.org/abs/2504.17718 en.wikipedia.org/wiki/Mathemati…
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    Applied Sheaf Theory For Multi-agent Artificial Intelligence...
    This paper provides a pedagogical introduction to classical sheaf theory and sheaf cohomology, followed by a research prospectus exploring potential applications to multi-agent artificial...
    7.1K
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    arXiv math.OC Optimization and Control
    @mathOCb
    Feb 13, 2024
    Ahmed Khaled, Chi Jin: Tuning-Free Stochastic Optimization arxiv.org/abs/2402.07793 arxiv.org/pdf/2402.07793
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    Tuning-Free Stochastic Optimization
    Large-scale machine learning problems make the cost of hyperparameter tuning ever more prohibitive. This creates a need for algorithms that can tune themselves on-the-fly. We formalize the notion...
    5.1K
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    arXiv math.OC Optimization and Control
    @mathOCb
    Dec 25, 2023
    Mengqi Hu, Bian Li, Yi-An Ma, Yifei Lou, Xiu Yang: A Gradient-Based Optimization Method Using the Koopman Operator arxiv.org/abs/2312.14361 arxiv.org/pdf/2312.14361
    1.8K
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    arXiv math.OC Optimization and Control
    @mathOCb
    Jun 22, 2022
    Thosten Koch, Timo Berthold, Jaap Pedersen, Charlie Vanaret: Progress in Mathematical Programming Solvers from 2001 to 2020 arxiv.org/abs/2206.09787 arxiv.org/pdf/2206.09787
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    Progress in Mathematical Programming Solvers from 2001 to 2020
    This study investigates the progress made in LP and MILP solver performance during the last two decades by comparing the solver software from the beginning of the millennium with the codes...
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    arXiv math.OC Optimization and Control
    @mathOCb
    Nov 27, 2023
    Yurii Nesterov: Primal subgradient methods with predefined stepsizes arxiv.org/abs/2311.13838 arxiv.org/pdf/2311.13838
    2K
  • user avatar
    arXiv math.OC Optimization and Control
    @mathOCb
    Jul 5, 2019
    Dimitris Bertsimas, Bartolomeo Stellato : Online Mixed-Integer Optimization in Milliseconds arxiv.org/abs/1907.02206 arxiv.org/pdf/1907.02206
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    Online Mixed-Integer Optimization in Milliseconds
    We propose a method to solve online mixed-integer optimization (MIO) problems at very high speed using machine learning. By exploiting the repetitive nature of online optimization, we are able to...

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