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Optimization and Learning

first-order methods; convex optimization; min-max optimization; optimization for deep learning

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What we are interested in

We study algorithms and theory for large-scale continuous optimization, with a focus on first-order methods for problems arising in modern machine learning. Our goal is to design methods that are provably convergent, efficient, and robust in practice. We work on projection-free conditional-gradient (Frank–Wolfe) variants for large-scale constrained problems, online and stochastic optimization, and robust and min–max formulations that capture distribution shift and adversarial settings. Furthermore, we study optimization for deep learning, including developing principled optimizers for training deep neural networks, methods for federated learning, and techniques that make training and inference more efficient and sustainable.

Researchers

Sebastian Pokutta
Department Head
pokutta (at) zib.de
Christophe Roux
Research Area Lead
roux (at) zib.de
Gábor Braun
braun (at) zib.de

Doctoral candidates

Jannis Halbey
halbey (at) zib.de
Deborah Hendrych
hendrych (at) zib.de

Undergraduate and Phase I Students

Dominik Kuzinowicz
kuzinowicz (at) zib.de

Former members

Visitors

  • Manuella Nakan Yopdup (September 2025–)
  • Mathieu Besançon (February 2025)

💬 Talks and posters

Conference and workshop talks

Jul 2025
Efficient Quadratic Corrections for Frank-Wolfe Algorithms by Jannis Halbey
22nd Conference on Advances in Continuous Optimization (EUROPT), Southampton
Jun 2025
Implicit Riemannian Optimism with Applications to Min-Max Problems by Christophe Roux
8th International Conference on Continuous Optimization (ICCOPT), Los Angeles
May 2025
Implicit Riemannian Optimism with Applications to Min-Max Problems by Christophe Roux
Foundations and Frontiers: Interdisciplinary Perspectives on Mathematical Optimization, Tokyo
Nov 2023
Bounding Geometric Penalties in First-order Riemannian Optimization by Christophe Roux
Seminar "Modern Methods in Applied Stochastics and Nonparametric Statistics", Berlin

Research seminar talks

Apr 2024
Bounding Geometric Penalties in Riemannian Optimization by Christophe Roux
CISPA Research seminar, Saarbrücken

Poster presentations

Jul 2025
Implicit Riemannian Optimism with Applications to Min-Max Problems by Christophe Roux
42nd International Conference on Machine Learning (ICML), Vancouver
May 2025
Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties by Christophe Roux
28th AISTATS Conference, Phuket
Jul 2024
Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point by Christophe Roux
41st International Conference on Machine Learning (ICML), Vienna

📝 Publications and preprints

Preprints

  1. Besançon, M., Designolle, S., Halbey, J., Hendrych, D., Kuzinowicz, D., Pokutta, S., Troppens, H., Viladrich, D., and Wirth, E. (2025). Improved Algorithms and Novel Applications of the FrankWolfe.jl Library. [arXiv]
    [BibTeX]
    @misc{2025_BesanconEtAl_Frankwolfelibrary,
  archiveprefix = {arXiv},
  eprint = {2501.14613},
  primaryclass = {math.OC},
  year = {2025},
  author = {Besançon, Mathieu and Designolle, Sébastien and Halbey, Jannis and Hendrych, Deborah and Kuzinowicz, Dominik and Pokutta, Sebastian and Troppens, Hannah and Viladrich, Daniel and Wirth, Elias},
  title = {Improved Algorithms and Novel Applications of the FrankWolfe.jl Library},
  date = {2025-01-24}
}
  2. Halbey, J., Rakotomandimby, S., Besançon, M., Designolle, S., and Pokutta, S. (2025). Efficient Quadratic Corrections for Frank-Wolfe Algorithms. [arXiv]
    [BibTeX]
    @misc{2025_HalbeyRakotomandimbyBesanconDesignollePokutta_Quadraticcorrections_2506-02635,
  archiveprefix = {arXiv},
  eprint = {2506.02635},
  primaryclass = {math.OC},
  year = {2025},
  author = {Halbey, Jannis and Rakotomandimby, Seta and Besançon, Mathieu and Designolle, Sébastien and Pokutta, Sebastian},
  title = {Efficient Quadratic Corrections for Frank-Wolfe Algorithms},
  date = {2025-06-03}
}
  3. Iommazzo, G., Martínez-Rubio, D., Criado, F., Wirth, E., and Pokutta, S. (2025). Linear Convergence of the Frank-Wolfe Algorithm Over Product Polytopes. [arXiv]
    [BibTeX]
    @misc{2025_IommazzoEtAl_Frankwolfeconvergence,
  archiveprefix = {arXiv},
  eprint = {2505.11259},
  primaryclass = {math.OC},
  year = {2025},
  author = {Iommazzo, Gabriele and Martínez-Rubio, David and Criado, Francisco and Wirth, Elias and Pokutta, Sebastian},
  title = {Linear Convergence of the Frank-Wolfe Algorithm Over Product Polytopes},
  date = {2025-05-16}
}
  4. Aigner, K.-M., Denzler, S., Liers, F., Pokutta, S., and Sharma, K. (2025). Scenario Reduction for Distributionally Robust Optimization. [arXiv]
    [BibTeX]
    @misc{2025_Kevin-martinEtAl_ScenarioreductionDro,
  archiveprefix = {arXiv},
  eprint = {2503.11484},
  primaryclass = {math.OC},
  year = {2025},
  author = {Aigner, Kevin-Martin and Denzler, Sebastian and Liers, Frauke and Pokutta, Sebastian and Sharma, Kartikey},
  title = {Scenario Reduction for Distributionally Robust Optimization},
  date = {2025-03-14}
}
  5. Pokutta, S. (2025). Scalable DC Optimization Via Adaptive Frank-Wolfe Algorithms. [arXiv]
    [BibTeX]
    @misc{2025_Pokutta_DcoptimizationFrankwolfe_2507-17545,
  archiveprefix = {arXiv},
  eprint = {2507.17545},
  primaryclass = {math.OC},
  year = {2025},
  author = {Pokutta, Sebastian},
  title = {Scalable DC Optimization Via Adaptive Frank-Wolfe Algorithms},
  date = {2025-07-23}
}
  6. Sharma, U., Goel, K., Dua, A., Pokutta, S., and Woodstock, Z. (2025). A Note on Asynchronous Projective Splitting in Julia. [URL]
    [BibTeX]
    @misc{2025_SharmaGoelDuaPokuttaWoodstock_AsyncProx,
  url = {https://zevwoodstock.github.io/media/publications/asyncprox.pdf},
  author = {Sharma, Utkarsh and Goel, Kashish and Dua, Aryan and Pokutta, Sebastian and Woodstock, Zev},
  title = {A Note on Asynchronous Projective Splitting in Julia},
  date = {2025-04-01},
  year = {2025}
}
  7. Takahashi, S., Pokutta, S., and Takeda, A. (2025). Accelerated Convergence of Frank–Wolfe Algorithms with Adaptive Bregman Step-Size Strategy. [arXiv]
    [BibTeX]
    @misc{2025_ShotaPokuttaAkiko_Frankwolfebregman,
  archiveprefix = {arXiv},
  eprint = {2504.04330},
  primaryclass = {math.OC},
  year = {2025},
  author = {Takahashi, Shota and Pokutta, Sebastian and Takeda, Akiko},
  title = {Accelerated Convergence of Frank--Wolfe Algorithms with Adaptive Bregman Step-Size Strategy},
  date = {2025-04-06}
}
  8. Wirth, E., Peña, J., and Pokutta, S. (2025). Adaptive Open-Loop Step-Sizes for Accelerated Convergence Rates of the Frank-Wolfe Algorithm. [arXiv]
    [BibTeX]
    @misc{2025_WirthJavierPokutta_Adaptivestepsizes,
  archiveprefix = {arXiv},
  eprint = {2505.09886},
  primaryclass = {math.OC},
  year = {2025},
  author = {Wirth, Elias and Peña, Javier and Pokutta, Sebastian},
  title = {Adaptive Open-Loop Step-Sizes for Accelerated Convergence Rates of the Frank-Wolfe Algorithm},
  date = {2025-05-15}
}
  9. Braun, G., Pokutta, S., and Woodstock, Z. (2024). Flexible Block-iterative Analysis for the Frank-Wolfe Algorithm. [arXiv]
    [BibTeX]
    @misc{2024_BraunPokuttaWoodstock_Blockiterativeanalysis,
  archiveprefix = {arXiv},
  eprint = {2409.06931},
  primaryclass = {math.OC},
  year = {2024},
  author = {Braun, Gábor and Pokutta, Sebastian and Woodstock, Zev},
  title = {Flexible Block-iterative Analysis for the Frank-Wolfe Algorithm},
  date = {2024-09-11}
}
  10. Scieur, D., Martínez-Rubio, D., Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2023). Strong Convexity of Sets in Riemannian Manifolds. [arXiv]
    [BibTeX]
    @misc{2022_KerdreuxScieurDaspremontPokutta_StrongconvexityRiemannianmanifolds,
  archiveprefix = {arXiv},
  eprint = {2312.03583},
  primaryclass = {math.OC},
  year = {2023},
  author = {Scieur, Damien and Martínez-Rubio, David and Kerdreux, Thomas and d'Aspremont, Alexandre and Pokutta, Sebastian},
  title = {Strong Convexity of Sets in Riemannian Manifolds},
  date = {2023-12-06}
}
  11. Braun, G., Carderera, A., Combettes, C., Hassani, H., Karbasi, A., Mokhtari, A., and Pokutta, S. (2022). Conditional Gradient Methods. [arXiv]
    [BibTeX]
    @misc{2022_BraunEtAl_Conditionalgradient,
  archiveprefix = {arXiv},
  eprint = {2211.14103},
  primaryclass = {math.OC},
  year = {2022},
  author = {Braun, Gábor and Carderera, Alejandro and Combettes, Cyrille and Hassani, Hamed and Karbasi, Amin and Mokhtari, Aryan and Pokutta, Sebastian},
  title = {Conditional Gradient Methods},
  date = {2022-11-25}
}
  12. Braun, G., Pokutta, S., and Weismantel, R. (2022). Alternating Linear Minimization: Revisiting von Neumann’s Alternating Projections. [arXiv] [slides] [video]
    [BibTeX]
    @misc{2022_BraunPokuttaWeismantel_Alternatingminimization,
  archiveprefix = {arXiv},
  eprint = {2212.02933},
  primaryclass = {math.OC},
  year = {2022},
  author = {Braun, Gábor and Pokutta, Sebastian and Weismantel, Robert},
  title = {Alternating Linear Minimization: Revisiting von Neumann's Alternating Projections},
  slides = {https://pokutta.com/slides/20230327-icerm.pdf},
  video = {https://icerm.brown.edu/programs/sp-s23/w2/#schedule-item-4945},
  date = {2022-12-06}
}
  13. Braun, G., and Pokutta, S. (2021). Dual Prices for Frank–Wolfe Algorithms. [arXiv]
    [BibTeX]
    @misc{2021_BraunPokutta_DualpricesFrankwolfe,
  archiveprefix = {arXiv},
  eprint = {2101.02087},
  primaryclass = {math.OC},
  year = {2021},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {Dual Prices for Frank–Wolfe Algorithms},
  date = {2021-01-06}
}
  14. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021). Local and Global Uniform Convexity Conditions. [arXiv]
    [BibTeX]
    @misc{2021_KerdrexDaspremontPokutta_LocalGlobalUniform,
  archiveprefix = {arXiv},
  eprint = {2102.05134},
  primaryclass = {math.OC},
  year = {2021},
  author = {Kerdreux, Thomas and d'Aspremont, Alexandre and Pokutta, Sebastian},
  title = {Local and Global Uniform Convexity Conditions},
  date = {2021-02-09}
}
  15. Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-bandit Strategies for Minimax Learning Problems. [arXiv]
    [BibTeX]
    @misc{2021_RouxWirthPokuttaKerdreux_Onlinebanditminimax,
  archiveprefix = {arXiv},
  eprint = {2105.13939},
  primaryclass = {cs.LG},
  year = {2021},
  author = {Roux, Christophe and Wirth, Elias and Pokutta, Sebastian and Kerdreux, Thomas},
  title = {Efficient Online-bandit Strategies for Minimax Learning Problems},
  date = {2021-05-28}
}
  16. Carderera, A., and Pokutta, S. (2020). Second-order Conditional Gradient Sliding. [arXiv] [code]
    [BibTeX]
    @misc{2020_CarderaPokutta_SecondOrderConditional,
  archiveprefix = {arXiv},
  eprint = {2002.08907},
  primaryclass = {math.OC},
  year = {2020},
  author = {Carderera, Alejandro and Pokutta, Sebastian},
  title = {Second-order Conditional Gradient Sliding},
  code = {https://github.com/pokutta/Second-order-Conditional-Gradients},
  date = {2020-02-20}
}
  17. Combettes, C., Spiegel, C., and Pokutta, S. (2020). Projection-free Adaptive Gradients for Large-scale Optimization. [arXiv] [summary] [code]
    [BibTeX]
    @misc{2020_CombettesSpiegelPokutta_Projectionfreeadaptivegradients,
  archiveprefix = {arXiv},
  eprint = {2009.14114},
  primaryclass = {math.OC},
  year = {2020},
  author = {Combettes, Cyrille and Spiegel, Christoph and Pokutta, Sebastian},
  title = {Projection-free Adaptive Gradients for Large-scale Optimization},
  code = {https://github.com/ZIB-IOL/StochasticFrankWolfe},
  summary = {https://pokutta.com/blog/research/2020/10/21/adasfw.html},
  date = {2020-09-29}
}
  18. Braun, G., and Pokutta, S. (2016). An Efficient High-probability Algorithm for Linear Bandits. [arXiv]
    [BibTeX]
    @misc{2016_BraunPokutta_Highprobabilitylinearbandits,
  archiveprefix = {arXiv},
  eprint = {1610.02072},
  primaryclass = {cs.DS},
  year = {2016},
  author = {Braun, Gábor and Pokutta, Sebastian},
  title = {An Efficient High-probability Algorithm for Linear Bandits},
  date = {2016-10-06}
}

Conference proceedings

  1. Martínez-Rubio, D., Roux, C., Criscitiello, C., and Pokutta, S. (2025). Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. Proceedings of the International Conference on Artificial Intelligence and Statistics. [arXiv]
    [BibTeX]
    @inproceedings{2023_MartinezrubioRouxCriscitielloPokutta_Riemannianminmax,
  year = {2025},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
  archiveprefix = {arXiv},
  eprint = {2305.16186},
  primaryclass = {math.OC},
  author = {Martínez-Rubio, David and Roux, Christophe and Criscitiello, Christopher and Pokutta, Sebastian},
  title = {Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties}
}
  2. Wirth, E., Besançon, M., and Pokutta, S. (2025). The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control. Proceedings of the International Conference on Artificial Intelligence and Statistics, 271–279. [URL] [arXiv]
    [BibTeX]
    @inproceedings{2024_WirthBesanconPokutta_Pivotingframework,
  year = {2025},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
  pages = {271--279},
  url = {https://proceedings.mlr.press/v258/besancon25a.html},
  archiveprefix = {arXiv},
  eprint = {2407.11760},
  primaryclass = {math.OC},
  author = {Wirth, Elias and Besançon, Mathieu and Pokutta, Sebastian},
  title = {The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control}
}
  3. Hendrych, D., Besançon, M., Martínez-Rubio, D., and Pokutta, S. (2025, January 30). Secant Line Search for Frank-Wolfe Algorithms. Proceedings of the International Conference on Machine Learning. [arXiv]
    [BibTeX]
    @inproceedings{2025_HendrychBesanconMartinezrubioPokutta_Secantfrankwolfe,
  year = {2025},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  archiveprefix = {arXiv},
  eprint = {2501.18775},
  primaryclass = {math.OC},
  author = {Hendrych, Deborah and Besançon, Mathieu and Martínez-Rubio, David and Pokutta, Sebastian},
  title = {Secant Line Search for Frank-Wolfe Algorithms},
  date = {2025-01-30}
}
  4. Roux, C., Martínez-Rubio, D., and Pokutta, S. (2025, January 30). Implicit Riemannian Optimism with Applications to Min-max Problems. Proceedings of the International Conference on Machine Learning. [arXiv]
    [BibTeX]
    @inproceedings{2025_RouxMartinezrubioPokutta_ImplicitRiemannian,
  year = {2025},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  archiveprefix = {arXiv},
  eprint = {2501.18381},
  primaryclass = {math.OC},
  author = {Roux, Christophe and Martínez-Rubio, David and Pokutta, Sebastian},
  title = {Implicit Riemannian Optimism with Applications to Min-max Problems},
  date = {2025-01-30}
}
  5. Martínez-Rubio, D., Roux, C., and Pokutta, S. (2024, March 15). Convergence and Trade-offs in Riemannian Gradient Descent and Riemannian Proximal Point. Proceedings of the International Conference on Machine Learning. [URL] [arXiv]
    [BibTeX]
    @inproceedings{2024_MartinezrubioRouxPokutta_Riemanniangradientdescent,
  year = {2024},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  url = {https://proceedings.mlr.press/v235/marti-nez-rubio24a.html},
  archiveprefix = {arXiv},
  eprint = {2403.10429},
  primaryclass = {math.OC},
  author = {Martínez-Rubio, David and Roux, Christophe and Pokutta, Sebastian},
  title = {Convergence and Trade-offs in Riemannian Gradient Descent and Riemannian Proximal Point},
  date = {2024-03-15}
}
  6. Martínez-Rubio, D., and Pokutta, S. (2023). Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties. Proceedings of the Conference on Learning Theory. [URL] [arXiv] [poster]
    [BibTeX]
    @inproceedings{2022_MartinezrubioPokutta_Acceleratedriemannian,
  year = {2023},
  booktitle = {Proceedings of the Conference on Learning Theory},
  url = {https://proceedings.mlr.press/v195/martinez-rubio23a/martinez-rubio23a.pdf},
  archiveprefix = {arXiv},
  eprint = {2211.14645},
  primaryclass = {math.OC},
  author = {Martínez-Rubio, David and Pokutta, Sebastian},
  title = {Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties},
  poster = {https://pokutta.com/slides/20221203_poster_neurips_riemannian.pdf}
}
  7. Wirth, E., Kera, H., and Pokutta, S. (2023). Approximate Vanishing Ideal Computations at Scale. Proceedings of the International Conference on Learning Representations. [arXiv] [slides]
    [BibTeX]
    @inproceedings{2022_WirthKeraPokutta_Approximatevanishingideal,
  year = {2023},
  booktitle = {Proceedings of the International Conference on Learning Representations},
  archiveprefix = {arXiv},
  eprint = {2207.01236},
  primaryclass = {cs.LG},
  author = {Wirth, Elias and Kera, Hiroshi and Pokutta, Sebastian},
  title = {Approximate Vanishing Ideal Computations at Scale},
  slides = {https://pokutta.com/slides/20220915_avi_at_scale.pdf}
}
  8. Wirth, E., Kerdreux, T., and Pokutta, S. (2023). Acceleration of Frank-Wolfe Algorithms with Open Loop Step-sizes. Proceedings of the International Conference on Artificial Intelligence and Statistics, 206, 77–100. [URL] [arXiv]
    [BibTeX]
    @inproceedings{2022_WirthKerdreuxPokutta_Frankwolfeacceleration,
  year = {2023},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
  volume = {206},
  pages = {77–100},
  url = {https://proceedings.mlr.press/v206/wirth23a.html},
  archiveprefix = {arXiv},
  eprint = {2205.12838},
  primaryclass = {math.OC},
  author = {Wirth, Elias and Kerdreux, Thomas and Pokutta, Sebastian},
  title = {Acceleration of Frank-Wolfe Algorithms with Open Loop Step-sizes}
}
  9. Criscitiello, C., Martínez-Rubio, D., and Boumal, N. (2023, July 24). Open Problem: Polynomial Linearly-convergent Method for G-convex Optimization? Proceedings of the Conference on Learning Theory. [arXiv]
    [BibTeX]
    @inproceedings{2023_CriscitielloMartinezrubioBoumal_Polynomialconvergence,
  year = {2023},
  booktitle = {Proceedings of the Conference on Learning Theory},
  archiveprefix = {arXiv},
  eprint = {2307.12743},
  primaryclass = {math.OC},
  author = {Criscitiello, Christopher and Martínez-Rubio, David and Boumal, Nicolas},
  title = {Open Problem: Polynomial Linearly-convergent Method for G-convex Optimization?},
  date = {2023-07-24}
}
  10. Martínez-Rubio, D., Wirth, E., and Pokutta, S. (2023, March 22). Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. Proceedings of the Conference on Learning Theory. [arXiv]
    [BibTeX]
    @inproceedings{2023_MartinezrubioWirthPokutta_Sparseapproximatepagerank,
  year = {2023},
  booktitle = {Proceedings of the Conference on Learning Theory},
  archiveprefix = {arXiv},
  eprint = {2303.12875},
  primaryclass = {math.OC},
  author = {Martínez-Rubio, David and Wirth, Elias and Pokutta, Sebastian},
  title = {Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond},
  date = {2023-03-22}
}
  11. Búi, M. N., Combettes, P. L., and Woodstock, Z. (2022). Block-activated Algorithms for Multicomponent Fully Nonsmooth Minimization. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 5428–5432. DOI: 10.1109/ICASSP43922.2022.9747479 [URL] [arXiv]
    [BibTeX]
    @inproceedings{2022_BiCombettesWoodstock_Blockactivatedminimization,
  year = {2022},
  booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing},
  month = may,
  pages = {5428-5432},
  doi = {10.1109/ICASSP43922.2022.9747479},
  url = {https://zevwoodstock.github.io/media/publications/icassp2022-2.pdf},
  archiveprefix = {arXiv},
  eprint = {2103.00520},
  primaryclass = {math.OC},
  author = {Búi, M. N. and Combettes, Patrick L. and Woodstock, Zev},
  title = {Block-activated Algorithms for Multicomponent Fully Nonsmooth Minimization}
}
  12. Criado, F., Martínez-Rubio, D., and Pokutta, S. (2022). Fast Algorithms for Packing Proportional Fairness and Its Dual. Proceedings of the Conference on Neural Information Processing Systems. [arXiv] [poster]
    [BibTeX]
    @inproceedings{2021_CriadoMartinezrubioPokutta_Packingproportionalfairness,
  year = {2022},
  booktitle = {Proceedings of the Conference on Neural Information Processing Systems},
  archiveprefix = {arXiv},
  eprint = {2109.03678},
  primaryclass = {math.OC},
  author = {Criado, Francisco and Martínez-Rubio, David and Pokutta, Sebastian},
  title = {Fast Algorithms for Packing Proportional Fairness and Its Dual},
  poster = {https://pokutta.com/slides/20211105_fairpacking-poster.pdf}
}
  13. Macdonald, J., Besançon, M., and Pokutta, S. (2022). Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. Proceedings of the International Conference on Machine Learning. [arXiv] [poster] [video]
    [BibTeX]
    @inproceedings{2021_MacdonaldBesanconPokutta_Interpretableneuralnetworks,
  year = {2022},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  archiveprefix = {arXiv},
  eprint = {2110.08105},
  primaryclass = {cs.LG},
  author = {Macdonald, Jan and Besançon, Mathieu and Pokutta, Sebastian},
  title = {Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings},
  poster = {https://pokutta.com/slides/20220712_icml_poster_interpretable_rde.pdf},
  video = {https://slideslive.com/38983588}
}
  14. Wirth, E., and Pokutta, S. (2022, February 7). Conditional Gradients for the Approximately Vanishing Ideal. Proceedings of the International Conference on Artificial Intelligence and Statistics. [arXiv] [summary] [poster] [code]
    [BibTeX]
    @inproceedings{2022_WirthPokutta_Conditionalgradients,
  year = {2022},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
  archiveprefix = {arXiv},
  eprint = {2202.03349},
  primaryclass = {cs.LG},
  author = {Wirth, Elias and Pokutta, Sebastian},
  title = {Conditional Gradients for the Approximately Vanishing Ideal},
  code = {https://github.com/ZIB-IOL/cgavi/},
  poster = {https://pokutta.com/slides/20220223_CGAVI_poster.pdf},
  summary = {https://pokutta.com/blog/research/2022/02/20/CGAVI.html},
  date = {2022-02-07}
}
  15. Carderera, A., Besançon, M., and Pokutta, S. (2021). Simple Steps Are All You Need: Frank-Wolfe and Generalized Self-concordant Functions. Proceedings of the Conference on Neural Information Processing Systems, 34, 5390–5401. [URL] [arXiv] [summary] [slides] [poster] [code]
    [BibTeX]
    @inproceedings{2024_CardereraBesanconPokutta_Scalablefrankwolfe:1,
  year = {2021},
  booktitle = {Proceedings of the Conference on Neural Information Processing Systems},
  month = may,
  volume = {34},
  pages = {5390–5401},
  url = {https://proceedings.neurips.cc/paper_files/paper/2021/file/2b323d6eb28422cef49b266557dd31ad-Paper.pdf},
  archiveprefix = {arXiv},
  eprint = {2105.13913},
  primaryclass = {math.OC},
  author = {Carderera, Alejandro and Besançon, Mathieu and Pokutta, Sebastian},
  title = {Simple Steps Are All You Need: Frank-Wolfe and Generalized Self-concordant Functions},
  code = {https://doi.org/10.5281/zenodo.4836009},
  poster = {https://pokutta.com/slides/20211120_poster_NeurIPS21_lSimple_steps_are_all_you_need.pdf},
  slides = {https://pokutta.com/slides/20210710_FW-simpleSteps-SelfConcordance.pdf},
  summary = {https://pokutta.com/blog/research/2021/10/09/self-concordant-abstract.html},
  date = {2021-05-28}
}
  16. Carderera, A., Diakonikolas, J., Lin, C. Y., and Pokutta, S. (2021, February 12). Parameter-free Locally Accelerated Conditional Gradients. Proceedings of the International Conference on Machine Learning. [arXiv] [slides]
    [BibTeX]
    @inproceedings{2021_CardereraDiakonikolasLinPokutta_ParameterfreeLocallyAccelerated,
  year = {2021},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  month = feb,
  archiveprefix = {arXiv},
  eprint = {2102.06806},
  primaryclass = {math.OC},
  author = {Carderera, Alejandro and Diakonikolas, Jelena and Lin, Cheuk Yin and Pokutta, Sebastian},
  title = {Parameter-free Locally Accelerated Conditional Gradients},
  slides = {https://pokutta.com/slides/20210716_PF_LaCG_Poster.pdf},
  date = {2021-02-12}
}
  17. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021, January). Projection-Free Optimization on Uniformly Convex Sets. Proceedings of the International Conference on Artificial Intelligence and Statistics. [arXiv] [slides]
    [BibTeX]
    @inproceedings{2021_KerdrexDaspremontPokutta_ProjectionFreeOptimization,
  year = {2021},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
  month = jan,
  archiveprefix = {arXiv},
  eprint = {2004.11053},
  primaryclass = {math.OC},
  author = {Kerdreux, Thomas and d'Aspremont, Alexandre and Pokutta, Sebastian},
  title = {Projection-Free Optimization on Uniformly Convex Sets},
  slides = {https://app.box.com/s/36wj0o8le96rrfdxec774wk7rrdp2vlm}
}
  18. Combettes, C., and Pokutta, S. (2020, March 13). Boosting Frank-Wolfe by Chasing Gradients. Proceedings of the International Conference on Machine Learning. [URL] [arXiv] [slides] [code] [video]
    [BibTeX]
    @inproceedings{2020_CombettesPokutta_BoostingFrankWolfe,
  year = {2020},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  month = mar,
  url = {https://proceedings.mlr.press/v119/combettes20a.html},
  archiveprefix = {arXiv},
  eprint = {2003.06369},
  primaryclass = {math.OC},
  author = {Combettes, Cyrille and Pokutta, Sebastian},
  title = {Boosting Frank-Wolfe by Chasing Gradients},
  code = {https://github.com/cyrillewcombettes/boostfw},
  slides = {https://app.box.com/s/wwj247r5d456q0778p9b9y1jm6txuifb},
  video = {https://youtube.com/watch?v=BfyV0C5FRbE},
  date = {2020-03-13}
}
  19. Diakonikolas, J., Carderera, A., and Pokutta, S. (2020). Locally Accelerated Conditional Gradients. Proceedings of the International Conference on Artificial Intelligence and Statistics. [URL] [arXiv] [slides] [code]
    [BibTeX]
    @inproceedings{2019_DiakonikolasCardereraPokutta_BreakingCurse:1,
  year = {2020},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
  url = {https://proceedings.mlr.press/v108/diakonikolas20a/diakonikolas20a.pdf},
  archiveprefix = {arXiv},
  eprint = {1906.07867},
  primaryclass = {math.OC},
  author = {Diakonikolas, Jelena and Carderera, Alejandro and Pokutta, Sebastian},
  title = {Locally Accelerated Conditional Gradients},
  code = {https://colab.research.google.com/drive/1ejjfCan7xnEhWWJXCIzb03CwQRG9iW_O},
  slides = {https://app.box.com/s/gphkhapso7d1vrfnzqykkb3vx0agxh8w}
}
  20. Pokutta, S., Singh, M., and Torrico Palacios, A. (2020, February 10). On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness. Proceedings of the International Conference on Machine Learning. [arXiv] [slides] [video]
    [BibTeX]
    @inproceedings{2019_PokuttaSinghTorrico_UnreasonableEffectiveness:1,
  year = {2020},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  archiveprefix = {arXiv},
  eprint = {2002.04063},
  primaryclass = {cs.DS},
  author = {Pokutta, Sebastian and Singh, Mohit and Torrico Palacios, Alfredo},
  title = {On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness},
  slides = {https://app.box.com/s/fmjxhfdpiqubm28upf563ohy8t8fgl3a},
  video = {https://youtube.com/watch?v=VB1e0HrDmVo},
  date = {2020-02-10}
}
  21. Braun, G., Pokutta, S., Tu, D., and Wright, S. (2019). Blended Conditional Gradients: the Unconditioning of Conditional Gradients. Proceedings of the International Conference on Machine Learning, 97, 735–743. [URL] [arXiv] [summary] [slides] [poster] [code]
    [BibTeX]
    @inproceedings{2018_BraunPokuttaTuStephen_Blendedconditionalgradients,
  year = {2019},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  volume = {97},
  pages = {735–743},
  url = {https://proceedings.mlr.press/v97/braun19a},
  archiveprefix = {arXiv},
  eprint = {1805.07311},
  primaryclass = {math.OC},
  author = {Braun, Gábor and Pokutta, Sebastian and Tu, Dan and Wright, Stephen},
  title = {Blended Conditional Gradients: the Unconditioning of Conditional Gradients},
  code = {https://github.com/pokutta/bcg},
  poster = {https://app.box.com/s/nmmm671jd72i397nysa8emfnzh1hn6hf},
  slides = {https://app.box.com/s/xbx3z7ws6dxvl3rzgj4jp6forigycooe},
  summary = {https://pokutta.com/blog/research/2019/02/18/bcg-abstract.html}
}
  22. Combettes, C., and Pokutta, S. (2019, April 28). Blended Matching Pursuit. Proceedings of the Conference on Neural Information Processing Systems. [URL] [arXiv] [slides] [code]
    [BibTeX]
    @inproceedings{2019_CombettesPokutta_BlendedMatching,
  year = {2019},
  booktitle = {Proceedings of the Conference on Neural Information Processing Systems},
  url = {https://papers.nips.cc/paper/8478-blended-matching-pursuit},
  archiveprefix = {arXiv},
  eprint = {1904.12335},
  primaryclass = {math.OC},
  author = {Combettes, Cyrille and Pokutta, Sebastian},
  title = {Blended Matching Pursuit},
  code = {https://colab.research.google.com/drive/17XYIxnCcJjKswba9mAaXFWnNGVZdsaXQ},
  slides = {https://app.box.com/s/8lfktq6h3dqp9t2gqydu2tp8h2uxgz7m},
  date = {2019-04-28}
}
  23. Diakonikolas, J., Carderera, A., and Pokutta, S. (2019, June 19). Breaking the Curse of Dimensionality (Locally) to Accelerate Conditional Gradients. Proceedings of the Optimization for Machine Learning. [URL] [arXiv] [slides] [code]
    [BibTeX]
    @inproceedings{2019_DiakonikolasCardereraPokutta_BreakingCurse,
  year = {2019},
  booktitle = {Proceedings of the Optimization for Machine Learning},
  url = {https://opt-ml.org/papers/2019/paper_26.pdf},
  archiveprefix = {arXiv},
  eprint = {1906.07867},
  primaryclass = {math.OC},
  author = {Diakonikolas, Jelena and Carderera, Alejandro and Pokutta, Sebastian},
  title = {Breaking the Curse of Dimensionality (Locally) to Accelerate Conditional Gradients},
  code = {https://colab.research.google.com/drive/1ejjfCan7xnEhWWJXCIzb03CwQRG9iW_O},
  slides = {https://app.box.com/s/gphkhapso7d1vrfnzqykkb3vx0agxh8w},
  date = {2019-06-19}
}
  24. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2019). Restarting Frank-Wolfe. Proceedings of the International Conference on Artificial Intelligence and Statistics. [URL] [arXiv] [slides]
    [BibTeX]
    @inproceedings{2019_KerdrexDaspremontPokutta_RestartingFrankWolfe:1,
  year = {2019},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics},
  url = {https://proceedings.mlr.press/v89/kerdreux19a/kerdreux19a.pdf},
  archiveprefix = {arXiv},
  eprint = {1810.02429},
  primaryclass = {math.OC},
  author = {Kerdreux, Thomas and d'Aspremont, Alexandre and Pokutta, Sebastian},
  title = {Restarting Frank-Wolfe},
  slides = {https://app.box.com/s/prd32r6xmuef2k4gah23rd0egllz9rv5}
}
  25. Pokutta, S., Singh, M., and Torrico Palacios, A. (2019). On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness. Proceedings of the Optimization for Machine Learning. [URL] [arXiv] [slides] [video]
    [BibTeX]
    @inproceedings{2019_PokuttaSinghTorrico_UnreasonableEffectiveness,
  year = {2019},
  booktitle = {Proceedings of the Optimization for Machine Learning},
  url = {https://opt-ml.org/papers/2019/paper_16.pdf},
  archiveprefix = {arXiv},
  eprint = {2002.04063},
  primaryclass = {cs.DS},
  author = {Pokutta, Sebastian and Singh, Mohit and Torrico Palacios, Alfredo},
  title = {On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness},
  slides = {https://app.box.com/s/fmjxhfdpiqubm28upf563ohy8t8fgl3a},
  video = {https://youtube.com/watch?v=VB1e0HrDmVo}
}
  26. Braun, G., Pokutta, S., and Zink, D. (2017). Lazifying Conditional Gradient Algorithms. Proceedings of the International Conference on Machine Learning, 70, 566–575. [URL] [arXiv] [slides] [poster]
    [BibTeX]
    @inproceedings{2016_BraunPokuttaZink_Lazifyinggradientalgorithms:1,
  year = {2017},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  volume = {70},
  pages = {566–575},
  url = {https://proceedings.mlr.press/v70/braun17a},
  archiveprefix = {arXiv},
  eprint = {1610.05120},
  primaryclass = {cs.DS},
  author = {Braun, Gábor and Pokutta, Sebastian and Zink, Daniel},
  title = {Lazifying Conditional Gradient Algorithms},
  poster = {https://app.box.com/s/lysscdg17ytpz7mqr0tu2djffyqvkl6a},
  slides = {https://app.box.com/s/zsp0hixjz2ha23u1vuyosijjkjdh8k}
}
  27. Lan, G., Pokutta, S., Zhou, Y., and Zink, D. (2017, March 16). Conditional Accelerated Lazy Stochastic Gradient Descent. Proceedings of the International Conference on Machine Learning. [URL] [arXiv]
    [BibTeX]
    @inproceedings{2017_LanPokuttaZhouZink_ConditionalAccelerated,
  year = {2017},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  url = {https://proceedings.mlr.press/v70/lan17a.html},
  archiveprefix = {arXiv},
  eprint = {1703.05840},
  primaryclass = {cs.LG},
  author = {Lan, G. and Pokutta, Sebastian and Zhou, Y and Zink, Daniel},
  title = {Conditional Accelerated Lazy Stochastic Gradient Descent},
  date = {2017-03-16}
}
  28. Roy, A., and Pokutta, S. (2016, October 28). Hierarchical Clustering Via Spreading Metrics. Proceedings of the Conference on Neural Information Processing Systems. [URL] [arXiv]
    [BibTeX]
    @inproceedings{2017_RoyPokutta_HierarchicalClustering:1,
  year = {2016},
  booktitle = {Proceedings of the Conference on Neural Information Processing Systems},
  url = {https://papers.nips.cc/paper/by-source-2016-1199},
  archiveprefix = {arXiv},
  eprint = {1610.09269},
  primaryclass = {cs.LG},
  author = {Roy, Aurko and Pokutta, Sebastian},
  title = {Hierarchical Clustering Via Spreading Metrics},
  date = {2016-10-28}
}
  29. Braun, G., Pokutta, S., and Xie, Y. (2014). Info-greedy Sequential Adaptive Compressed Sensing. Proceedings of the Allerton Conference on Communication, Control, and Computing, 858–865. DOI: 10.1109/ALLERTON.2014.7028544 [arXiv]
    [BibTeX]
    @inproceedings{2014_BraunPokuttaYao_Infogreedycompressedsensing:1,
  year = {2014},
  booktitle = {Proceedings of the Allerton Conference on Communication, Control, and Computing},
  pages = {858–865},
  doi = {10.1109/ALLERTON.2014.7028544},
  archiveprefix = {arXiv},
  eprint = {1407.0731},
  primaryclass = {cs.IT},
  author = {Braun, Gábor and Pokutta, Sebastian and Xie, Yao},
  title = {Info-greedy Sequential Adaptive Compressed Sensing},
  date = {2014-07-02}
}

Full articles

  1. Carderera, A., Pokutta, S., Schütte, C., and Weiser, M. (2025). An Efficient First-order Conditional Gradient Algorithm in Data-driven Sparse Identification of Nonlinear Dynamics to Solve Sparse Recovery Problems Under Noise. Journal of Computational and Applied Mathematics. DOI: 10.1016/j.cam.2025.116675 [arXiv]
    [BibTeX]
    @article{2021_CardereraPokuttaSchutteWeiser_CINDy,
  year = {2025},
  journal = {Journal of Computational and Applied Mathematics},
  date = {2025-04-01},
  month = apr,
  doi = {10.1016/j.cam.2025.116675},
  archiveprefix = {arXiv},
  eprint = {2101.02630},
  primaryclass = {math.DS},
  author = {Carderera, Alejandro and Pokutta, Sebastian and Schütte, Christof and Weiser, Martin},
  title = {An Efficient First-order Conditional Gradient Algorithm in Data-driven Sparse Identification of Nonlinear Dynamics to Solve Sparse Recovery Problems Under Noise}
}
  2. Wirth, E., Pena, J., and Pokutta, S. (2025). Accelerated Affine-invariant Convergence Rates of the Frank-Wolfe Algorithm with Open-loop Step-sizes. Mathematical Programming. [arXiv]
    [BibTeX]
    @article{2023_WirthJavierPokutta_Affineinvariantconvergence,
  year = {2025},
  journal = {Mathematical Programming},
  archiveprefix = {arXiv},
  eprint = {2310.04096},
  primaryclass = {math.OC},
  author = {Wirth, Elias and Pena, Javier and Pokutta, Sebastian},
  title = {Accelerated Affine-invariant Convergence Rates of the Frank-Wolfe Algorithm with Open-loop Step-sizes}
}
  3. Woodstock, Z., and Pokutta, S. (2025). Splitting the Conditional Gradient Algorithm. SIAM Journal on Optimization, 35(1), 347–368. DOI: 10.1137/24M1638008 [arXiv]
    [BibTeX]
    @article{2023_WoodstockPokutta_Conditionalgradientnonconvex,
  year = {2025},
  journal = {SIAM Journal on Optimization},
  volume = {35},
  number = {1},
  pages = {347-368},
  doi = {10.1137/24M1638008},
  archiveprefix = {arXiv},
  eprint = {2311.05381},
  primaryclass = {math.OC},
  author = {Woodstock, Zev and Pokutta, Sebastian},
  title = {Splitting the Conditional Gradient Algorithm}
}
  4. Wirth, E., Peña, J., and Pokutta, S. (2025). Fast Convergence of Frank-Wolfe Algorithms on Polytopes. INFORMS Journal on Mathematics of Operations Research. DOI: 10.1287/moor.2024.0580 [URL] [arXiv]
    [BibTeX]
    @article{2024_WirthJavierPokutta_Frankwolfeconvergence,
  year = {2025},
  journal = {INFORMS Journal on Mathematics of Operations Research},
  date = {2025-05-29},
  doi = {10.1287/moor.2024.0580},
  url = {https://pubsonline.informs.org/doi/abs/10.1287/moor.2024.0580},
  archiveprefix = {arXiv},
  eprint = {2406.18789},
  primaryclass = {math.OC},
  author = {Wirth, Elias and Peña, Javier and Pokutta, Sebastian},
  title = {Fast Convergence of Frank-Wolfe Algorithms on Polytopes}
}
  5. Carderera, A., Besançon, M., and Pokutta, S. (2024). Scalable Frank-Wolfe on Generalized Self-concordant Functions Via Simple Steps. SIAM Journal on Optimization, 34(3), 2231–2258. DOI: 10.1137/23M1616789 [arXiv] [summary] [slides] [poster] [code]
    [BibTeX]
    @article{2024_CardereraBesanconPokutta_Scalablefrankwolfe,
  year = {2024},
  journal = {SIAM Journal on Optimization},
  date = {2024-07-02},
  month = sep,
  volume = {34},
  number = {3},
  pages = {2231-2258},
  doi = {10.1137/23M1616789},
  archiveprefix = {arXiv},
  eprint = {2105.13913},
  primaryclass = {math.OC},
  author = {Carderera, Alejandro and Besançon, Mathieu and Pokutta, Sebastian},
  title = {Scalable Frank-Wolfe on Generalized Self-concordant Functions Via Simple Steps},
  code = {https://doi.org/10.5281/zenodo.4836009},
  poster = {https://pokutta.com/slides/20211120_poster_NeurIPS21_lSimple_steps_are_all_you_need.pdf},
  slides = {https://pokutta.com/slides/20210710_FW-simpleSteps-SelfConcordance.pdf},
  summary = {https://pokutta.com/blog/research/2021/10/09/self-concordant-abstract.html}
}
  6. Braun, G., Guzmán, C., and Pokutta, S. (2024). Corrections to “Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization Via Information Theory.” IEEE Transactions on Information Theory, 70, 5408–5409. DOI: 10.1109/TIT.2024.3357200
    [BibTeX]
    @article{2024_BraunGuzmanPokutta_Oraclecomplexity,
  year = {2024},
  journal = {IEEE Transactions on Information Theory},
  date = {2024-06-21},
  month = jul,
  volume = {70},
  pages = {5408-5409},
  doi = {10.1109/TIT.2024.3357200},
  author = {Braun, Gábor and Guzmán, Cristóbal and Pokutta, Sebastian},
  title = {Corrections to “Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization Via Information Theory”}
}
  7. Pokutta, S. (2023). The Frank-Wolfe Algorithm: a Short Introduction. Jahresbericht Der Deutschen Mathematiker-Vereinigung, 126(1), 3–35. DOI: 10.1365/s13291-023-00275-x [URL] [arXiv]
    [BibTeX]
    @article{2023_Pokutta_Frankwolfealgorithm,
  year = {2024},
  journal = {Jahresbericht der Deutschen Mathematiker-Vereinigung},
  date = {2023-12-13},
  month = mar,
  volume = {126},
  number = {1},
  pages = {3–35},
  doi = {10.1365/s13291-023-00275-x},
  url = {https://link.springer.com/article/10.1365/s13291-023-00275-x/fulltext.html},
  archiveprefix = {arXiv},
  eprint = {2311.05313},
  primaryclass = {math.OC},
  author = {Pokutta, Sebastian},
  title = {The Frank-Wolfe Algorithm: a Short Introduction}
}
  8. Besançon, M., Dias Garcia, J., Legat, B., and Sharma, A. (2023). Flexible Differentiable Optimization Via Model Transformations. INFORMS Journal on Computing. DOI: 10.1287/ijoc.2022.0283 [URL] [arXiv]
    [BibTeX]
    @article{2022_BesanconJoaquimBenotAkshay_Differentiableoptimization,
  year = {2023},
  journal = {INFORMS Journal on Computing},
  month = aug,
  doi = {10.1287/ijoc.2022.0283},
  url = {https://pubsonline.informs.org/doi/epdf/10.1287/ijoc.2022.0283},
  archiveprefix = {arXiv},
  eprint = {2206.06135},
  primaryclass = {cs.LG},
  author = {Besançon, Mathieu and Dias Garcia, Joaquim and Legat, Benoît and Sharma, Akshay},
  title = {Flexible Differentiable Optimization Via Model Transformations}
}
  9. Hunkenschröder, C., Pokutta, S., and Weismantel, R. (2023). Minimizing a Low-dimensional Convex Function Over a High-dimensional Cube. SIAM Journal on Optimization, 33(2), 538–552. DOI: 10.1137/22M1489988 [arXiv]
    [BibTeX]
    @article{2022_HunkenschrderPokuttaWeismantel_Convexoptimization,
  year = {2023},
  journal = {SIAM Journal on Optimization},
  date = {2023-05-26},
  month = jun,
  volume = {33},
  number = {2},
  pages = {538-552},
  doi = {10.1137/22M1489988},
  archiveprefix = {arXiv},
  eprint = {2204.05266},
  primaryclass = {math.OC},
  author = {Hunkenschröder, Christoph and Pokutta, Sebastian and Weismantel, Robert},
  title = {Minimizing a Low-dimensional Convex Function Over a High-dimensional Cube}
}
  10. Combettes, C., and Pokutta, S. (2023). Revisiting the Approximate Carathéodory Problem Via the Frank-Wolfe Algorithm. Mathematical Programming A, 197, 191–214. DOI: 10.1007/s10107-021-01735-x [URL] [arXiv] [slides] [code] [video]
    [BibTeX]
    @article{2023_CombettesPokutta_RevisitingApproximateCaratheodory,
  year = {2023},
  journal = {Mathematical Programming A},
  volume = {197},
  pages = {191-214},
  doi = {10.1007/s10107-021-01735-x},
  url = {https://rdcu.be/cCnPL},
  archiveprefix = {arXiv},
  eprint = {1911.04415},
  primaryclass = {math.OC},
  author = {Combettes, Cyrille and Pokutta, Sebastian},
  title = {Revisiting the Approximate Carathéodory Problem Via the Frank-Wolfe Algorithm},
  code = {https://colab.research.google.com/drive/1GLGRTc2jFYy9CqqoVnZgIFVQgAC0c3aZ},
  slides = {https://app.box.com/s/f0zuvr45qa6etidd06i1wart58o7bc8t},
  video = {https://youtube.com/watch?v=VB1e0HrDmVo}
}
  11. Kreimeier, T., Pokutta, S., Walther, A., and Woodstock, Z. (2023). On a Frank-Wolfe Approach for Abs-smooth Functions. Optimization Methods and Software. [arXiv]
    [BibTeX]
    @article{2023_KreimeierPokuttaWaltherWoodstock_Frankwolfeabssmooth,
  year = {2023},
  journal = {Optimization Methods and Software},
  archiveprefix = {arXiv},
  eprint = {2303.09881},
  primaryclass = {math.OC},
  author = {Kreimeier, Timo and Pokutta, Sebastian and Walther, Andrea and Woodstock, Zev},
  title = {On a Frank-Wolfe Approach for Abs-smooth Functions},
  date = {2023-03-17}
}
  12. Wilken, S. E., Besançon, M., Kratochvíl, M., Kuate, C. A. F., Trefois, C., Gu, W., and Ebenhöh, O. (2022). Interrogating the Effect of Enzyme Kinetics on Metabolism Using Differentiable Constraint-based Models. Metabolic Engineering.
    [BibTeX]
    @article{2022_ElmoEtAl_Enzymekineticsmetabolism,
  year = {2022},
  journal = {Metabolic Engineering},
  month = nov,
  author = {Wilken, St. Elmo and Besançon, Mathieu and Kratochvíl, Miroslav and Kuate, Chilperic Armel Foko and Trefois, Christophe and Gu, Wei and Ebenhöh, Oliver},
  title = {Interrogating the Effect of Enzyme Kinetics on Metabolism Using Differentiable Constraint-based Models}
}
  13. Besançon, M., Carderera, A., and Pokutta, S. (2022). FrankWolfe.jl: A High-Performance and Flexible Toolbox for Frank-Wolfe Algorithms and Conditional Gradients. INFORMS Journal on Computing. [arXiv] [summary] [slides] [code]
    [BibTeX]
    @article{2021_BesanconCardereraPokutta_Frankwolfetoolbox,
  year = {2022},
  journal = {INFORMS Journal on Computing},
  month = feb,
  archiveprefix = {arXiv},
  eprint = {2104.06675},
  primaryclass = {math.OC},
  author = {Besançon, Mathieu and Carderera, Alejandro and Pokutta, Sebastian},
  title = {FrankWolfe.jl: A High-Performance and Flexible Toolbox for Frank-Wolfe Algorithms and Conditional Gradients},
  code = {https://github.com/ZIB-IOL/FrankWolfe.jl},
  slides = {https://pokutta.com/slides/20210710_FW-simpleSteps-SelfConcordance.pdf},
  summary = {https://pokutta.com/blog/research/2021/04/20/FrankWolfejl.html}
}
  14. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2022). Restarting Frank-Wolfe. Journal of Optimization Theory and Applications, 192, 799–829. DOI: 10.1007/s10957-021-01989-7 [URL] [arXiv] [slides]
    [BibTeX]
    @article{2019_KerdrexDaspremontPokutta_RestartingFrankWolfe,
  year = {2022},
  journal = {Journal of Optimization Theory and Applications},
  volume = {192},
  pages = {799-829},
  doi = {10.1007/s10957-021-01989-7},
  url = {https://dx.doi.org/10.1007/s10957-021-01989-7},
  archiveprefix = {arXiv},
  eprint = {1810.02429},
  primaryclass = {math.OC},
  author = {Kerdreux, Thomas and d'Aspremont, Alexandre and Pokutta, Sebastian},
  title = {Restarting Frank-Wolfe},
  slides = {https://app.box.com/s/prd32r6xmuef2k4gah23rd0egllz9rv5}
}
  15. Combettes, C., and Pokutta, S. (2021). Complexity of Linear Minimization and Projection on Some Sets. Operations Research Letters, 49(4). [arXiv] [code]
    [BibTeX]
    @article{2021_CombettesPokutta_ComplexityLinearMinimization,
  year = {2021},
  journal = {Operations Research Letters},
  month = jul,
  volume = {49},
  number = {4},
  archiveprefix = {arXiv},
  eprint = {2101.10040},
  primaryclass = {math.OC},
  author = {Combettes, Cyrille and Pokutta, Sebastian},
  title = {Complexity of Linear Minimization and Projection on Some Sets},
  code = {https://github.com/cyrillewcombettes/complexity},
  date = {2021-01-25}
}
  16. Kerdreux, T., Roux, C., d’Aspremont, A., and Pokutta, S. (2021). Linear Bandits on Uniformly Convex Sets. Journal of Machine Learning Research, 22(284), 1–23. [URL] [arXiv] [summary]
    [BibTeX]
    @article{2021_KerdreuxRouxDaspremontPokutta_Linearbandits,
  year = {2021},
  journal = {Journal of Machine Learning Research},
  month = mar,
  volume = {22},
  number = {284},
  pages = {1–23},
  url = {http://jmlr.org/papers/v22/21-0277.html},
  archiveprefix = {arXiv},
  eprint = {2103.05907},
  primaryclass = {cs.LG},
  author = {Kerdreux, Thomas and Roux, Christophe and d'Aspremont, Alexandre and Pokutta, Sebastian},
  title = {Linear Bandits on Uniformly Convex Sets},
  summary = {https://www.pokutta.com/blog/research/2021/04/03/linearBandits.html},
  date = {2021-03-10}
}
  17. Braun, G., Pokutta, S., and Zink, D. (2019). Lazifying Conditional Gradient Algorithms. The Journal of Machine Learning Research, 20(71), 1–42. [URL] [arXiv]
    [BibTeX]
    @article{2016_BraunPokuttaZink_Lazifyinggradientalgorithms,
  year = {2019},
  journal = {The Journal of Machine Learning Research},
  volume = {20},
  number = {71},
  pages = {1–42},
  url = {https://jmlr.org/papers/v20/18-114.html},
  archiveprefix = {arXiv},
  eprint = {1610.05120},
  primaryclass = {cs.DS},
  author = {Braun, Gábor and Pokutta, Sebastian and Zink, Daniel},
  title = {Lazifying Conditional Gradient Algorithms}
}
  18. Braun, G., Guzmán, C., and Pokutta, S. (2017). Unifying Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization. IEEE Transactions on Information Theory, 63(7), 4709–4724. DOI: 10.1109/TIT.2017.2701343 [arXiv]
    [BibTeX]
    @article{2014_BraunGuzmanPokutta_Lowerboundsoraclecomplexity,
  year = {2017},
  journal = {IEEE Transactions on Information Theory},
  month = jul,
  volume = {63},
  number = {7},
  pages = {4709-4724},
  doi = {10.1109/TIT.2017.2701343},
  archiveprefix = {arXiv},
  eprint = {1407.5144},
  primaryclass = {math.OC},
  author = {Braun, Gábor and Guzmán, Cristóbal and Pokutta, Sebastian},
  title = {Unifying Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization}
}
  19. Braun, G., Jain, R., Lee, T., and Pokutta, S. (2017). Information-theoretic Approximations of the Nonnegative Rank. Computational Complexity, 26, 147–197. DOI: 10.1007/s00037-016-0125-z [URL]
    [BibTeX]
    @article{2017_BraunRahulTroyPokutta_Informationtheoreticnonnegativerank,
  year = {2017},
  journal = {Computational Complexity},
  volume = {26},
  pages = {147–197},
  doi = {10.1007/s00037-016-0125-z},
  url = {https://eccc.weizmann.ac.il/report/2013/158},
  author = {Braun, Gábor and Jain, Rahul and Lee, Troy and Pokutta, Sebastian},
  title = {Information-theoretic Approximations of the Nonnegative Rank}
}
  20. Roy, A., and Pokutta, S. (2017). Hierarchical Clustering Via Spreading Metrics. Journal of Machine Learning Research, 18, 1–35. [URL] [arXiv]
    [BibTeX]
    @article{2017_RoyPokutta_HierarchicalClustering,
  year = {2017},
  journal = {Journal of Machine Learning Research},
  volume = {18},
  pages = {1-35},
  url = {https://jmlr.org/papers/v18/17-081.html},
  archiveprefix = {arXiv},
  eprint = {1610.09269},
  primaryclass = {cs.LG},
  author = {Roy, Aurko and Pokutta, Sebastian},
  title = {Hierarchical Clustering Via Spreading Metrics}
}
  21. Braun, G., Pokutta, S., and Xie, Y. (2015). Info-greedy Sequential Adaptive Compressed Sensing. IEEE Journal of Selected Topics in Signal Processing, 9(4), 601–611. DOI: 10.1109/JSTSP.2015.2400428 [arXiv]
    [BibTeX]
    @article{2014_BraunPokuttaYao_Infogreedycompressedsensing,
  year = {2015},
  journal = {IEEE Journal of Selected Topics in Signal Processing},
  month = jun,
  volume = {9},
  number = {4},
  pages = {601–611},
  doi = {10.1109/JSTSP.2015.2400428},
  archiveprefix = {arXiv},
  eprint = {1407.0731},
  primaryclass = {cs.IT},
  author = {Braun, Gábor and Pokutta, Sebastian and Xie, Yao},
  title = {Info-greedy Sequential Adaptive Compressed Sensing}
}

🔬 Projects

Ongoing Projects

On a Frank-Wolfe Approach for Abs-smooth Optimization

Motivated by nonsmooth problems in machine learning, we solve the problem of minimizing an abs-smooth function subject to closed convex constraints. New theory and algorithms are developed using linear minimization oracles to enforce constraints and abs-linearization methods to handle nonsmoothness.

MATH+ EF1-23
Apr 2023 to Mar 2026
4
5

Completed Projects

Sparsity and Sample-size Efficiency in Structured Learning

In this project, we study algorithms that promote sparsity. We develop PageRank optimization algorithms that scale with solution sparsity and investigate Riemannian optimization using manifold geometry. Additionally, we develop algorithms for efficient fair resource allocation based on established fairness axioms.

MATH+ AA5-1
Jan 2022 to Dec 2023
2
9