Sebastian Pokutta

Artificial Intelligence, Optimization, and Machine Learning. Professor @TUBerlin, Vice President @ZuseInstitute, Chair @MATH+, Chair @MODAL

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In Preparation / Articles Pending Review ⏵

  1. Haase, J., Hanel, P. H. P., and Pokutta, S. (2025). Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability. Preprint. [arXiv] haiimlsocial
  2. Takahashi, S., Pokutta, S., and Takeda, A. (2025). Accelerated Convergence of Frank–Wolfe Algorithms with Adaptive Bregman Step-Size Strategy. Preprint. [arXiv] fwopt
  3. Sharma, U., Goel, K., Dua, A., Pokutta, S., and Woodstock, Z. (2025). A note on asynchronous Projective Splitting in Julia. Preprint. [arXiv] opt
  4. Haase, J., Klessascheck, F., Mendling, J., and Pokutta, S. (2025). Sustainability via LLM Right-sizing. Preprint. [arXiv] haiimlsustainability
  5. Aigner, K.-M., Denzler, S., Liers, F., Pokutta, S., and Sharma, K. (2025). Scenario Reduction for Distributionally Robust Optimization. Preprint. [arXiv] optrobopt
  6. Martínez-Rubio, D., and Pokutta, S. (2025). Beyond Short Steps in Frank-Wolfe Algorithms. Preprint. [arXiv] mlopt
  7. Besançon, M., Designolle, S., Halbey, J., Hendrych, D., Kuzinowicz, D., Pokutta, S., Troppens, H., Viladrich Herrmannsdoerfer, D., and Wirth, E. (2025). Improved algorithms and novel applications of the FrankWolfe.jl library. Preprint. [arXiv] optsoftware
  8. Mexi, G., Kamp, D., Shinano, Y., Pu, S., Hoen, A., Bestuzheva, K., Hojny, C., Walter, M., Pfetsch, M. E., Pokutta, S., and Koch, T. (2025). State-of-the-art Methods for Pseudo-Boolean Solving with SCIP. Preprint. [arXiv] ipoptsoftware
  9. Tadinada, S., Siebert, T., Fuhrmann, J., Pokutta, S., and Walther, A. (2024). An AD-enabled Frank-Wolfe method for non-smooth optimization. Submitted. opt
  10. Haase, J., and Pokutta, S. (2024). Human-AI Co-Creativity: Exploring Synergies Across Levels of Creative Collaboration. Preprint. [arXiv] haiimlsocial
  11. Głuch, G., Turan, B., Nagarajan, S. G., and Pokutta, S. (2024). The Good, the Bad and the Ugly: Watermarks, Transferable Attacks and Adversarial Defenses. Preprint. [arXiv] [summary] [poster] mlxai
  12. Braun, G., Pokutta, S., and Woodstock, Z. (2024). Flexible block-iterative analysis for the Frank-Wolfe algorithm. Preprint. [arXiv] mlopt
  13. Designolle, S., Vértesi, T., and Pokutta, S. (2024). Better bounds on Grothendieck constants of finite orders. Preprint. [arXiv] optphysicsquantum
  14. Göß, A., Martin, A., Pokutta, S., and Sharma, K. (2024). Norm-induced Cuts: Optimization with Lipschitzian Black-box Functions. Preprint. [arXiv] ipopt
  15. Sharma, K., Hendrych, D., Besançon, M., and Pokutta, S. (2024). Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe. Preprint. [arXiv] [code] ipopt
  16. Zimmer, M., Andoni, M., Spiegel, C., and Pokutta, S. (2023). PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs. Preprint. [arXiv] [code] mlsparsity
  17. Kiem, A., Pokutta, S., and Spiegel, C. (2023). The Four-Color Ramsey Multiplicity of Triangles. Preprint. [arXiv] [code] combinatoricsgraphs
  18. Braun, G., Pokutta, S., and Weismantel, R. (2022). Alternating Linear Minimization: Revisiting von Neumann’s alternating projections. Preprint. [arXiv] [slides] [video] opt
  19. Gelß, P., Klus, S., Shakibaei, Z., and Pokutta, S. (2022). Low-rank tensor decompositions of quantum circuits. Preprint. [arXiv] quantum
  20. Kerdreux, T., Scieur, D., Martinez-Rubio, D., d’Aspremont, A., and Pokutta, S. (2022). Strong Convexity of Sets in Riemannian Manifolds. Preprint. [arXiv] mlopt
  21. Pokutta, S., Spiegel, C., and Zimmer, M. (2020). Deep Neural Network Training with Frank-Wolfe. Preprint. [arXiv] [summary] [code] mloptsparsity
  22. Combettes, C. W., Spiegel, C., and Pokutta, S. (2020). Projection-Free Adaptive Gradients for Large-Scale Optimization. Preprint. [arXiv] [summary] [code] mlopt
  23. Bärmann, A., Martin, A., Pokutta, S., and Schneider, O. (2018). An Online-Learning Approach to Inverse Optimization. Submitted. [arXiv] [summary] [slides] ipmlopt

Books and Edited Volumes ⏵

  1. Fackeldey, K., Kannan, A., Pokutta, S., Sharma, K., Walter, D., Walther, A., and Weiser, M. (Eds.). (2025). Mathematical Optimization for Machine Learning. de Gruyter. mlopt (Proceedings of MATH+ TES Summer Semester 2023)
  2. Braun, G., Carderera, A., Combettes, C. W., Hassani, H., Karbasi, A., Mokthari, A., and Pokutta, S. (2025). Conditional Gradient Methods. to appear in MOS-SIAM Series on Optimization. [arXiv] mloptsurvey

Refereed Conference Proceedings ⏵

  1. Pelleriti, N., Zimmer, M., Wirth, E., and Pokutta, S. (2025). Approximating Latent Manifolds in Neural Networks via Vanishing Ideals. To Appear in Proceedings of ICML. [arXiv] compalgmltheory
  2. Pauls, J., Zimmer, M., Turan, B., Saatchi, S., Ciais, P., Pokutta, S., and Gieseke, F. (2025). Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation. To Appear in Proceedings of ICML. [arXiv] [visuals] ai4sciencemlsustainability
  3. Hendrych, D., Besançon, M., Martínez-Rubio, D., and Pokutta, S. (2025). Secant Line Search for Frank-Wolfe Algorithms. To Appear in Proceedings of ICML. [arXiv] opt
  4. Roux, C., Martínez-Rubio, D., and Pokutta, S. (2025). Implicit Riemannian Optimism with Applications to Min-Max Problems. To Appear in Proceedings of ICML. [arXiv] mlopt
  5. Mundinger, K., Zimmer, M., Kiem, A., Spiegel, C., and Pokutta, S. (2025). Neural Discovery in Mathematics: Do Machines Dream of Colored Planes? To Appear in Proceedings of ICML. [arXiv] ai4sciencedggraphs (Spotlight + Conference Proceedings)
  6. Troppens, H., Besançon, M., Wilken, S. E., and Pokutta, S. (2025). Mixed-Integer Optimization for Loopless Flux Distributions in Metabolic Networks. To Appear in Proceedings of Proceedings of Symposium on Experimental Algorithms (SEA) 2025. [arXiv] ai4sciencebiochemistryopt
  7. Zimmer, M., Spiegel, C., and Pokutta, S. (2025). Compression-aware Training of Neural Networks using Frank-Wolfe. In K. Fackeldey, A. Kannan, S. Pokutta, K. Sharma, D. Walter, A. Walther, and M. Weiser (Eds.), Mathematical Optimization for Machine Learning (pp. 137–168). De Gruyter. [PDF] [arXiv] mloptsparsity
  8. Lasby, M., Zimmer, M., Pokutta, S., and Schultheis, E. (2025). Compressed sparse tiles for memory-efficient unstructured and semi-structured sparsity. Proceedings of ICLR 2025 Workshop on Sparsity in LLMs (SLLM). [PDF] [conference] hpcml
  9. Głuch, G., Turan, B., Nagarajan, S. G., and Pokutta, S. (2025). The Good, the Bad and the Ugly: Watermarks, Transferable Attacks and Adversarial Defenses. Proceedings of ICLR 2025 Workshop on GenAI Watermarking (WMARK). [arXiv] [summary] [poster] [conference] mlxai
  10. Sadiku, S., Wagner, M., and Pokutta, S. (2025). Group-wise Sparse and Explainable Adversarial Attacks. To Appear in Proceedings of ICLR. [arXiv] [poster] ml
  11. Roux, C., Zimmer, M., and Pokutta, S. (2025). On the Byzantine-Resilience of Distillation-Based Federated Learning. To Appear in Proceedings of ICLR. [arXiv] [summary] [code] mlopt
  12. Sadiku, S., Wagner, M., Nagarajan, S. G., and Pokutta, S. (2025). S-CFE: Simple Counterfactual Explanations. To Appear in Proceedings of AISTATS. [arXiv] mlxai
  13. Wirth, E., Besançon, M., and Pokutta, S. (2025). The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control. To Appear in Proceedings of AISTATS. [arXiv] mlopt (Oral Presentation + Conference Proceedings)
  14. Martinez-Rubio, D., Roux, C., Criscitiello, C., and Pokutta, S. (2025). Accelerated Methods for Riemannian Min-Max Problems. To Appear in Proceedings of AISTATS. [arXiv] mlopt
  15. Pauls, J., Zimmer, M., Kelly, U. M., Schwartz, M., Saatchi, S., Ciais, P., Pokutta, S., Brandt, M., and Gieseke, F. (2024). Estimating Canopy Height at Scale. Proceedings of ICML. [arXiv] [code] [visuals] ai4sciencemlsustainability
  16. Kiem, A., Pokutta, S., and Spiegel, C. (2024). Categorification of Flag Algebras. Proceedings of Discrete Mathematics Days. [arXiv] combinatoricsgraphs
  17. Kiem, A., Pokutta, S., and Spiegel, C. (2024). The Four-Color Ramsey Multiplicity of Triangles. Proceedings of Discrete Mathematics Days. [PDF] [arXiv] [code] combinatoricsgraphs
  18. Mundinger, K., Pokutta, S., Spiegel, C., and Zimmer, M. (2024). Extending the Continuum of Six-Colorings. Proceedings of Discrete Mathematics Days. [arXiv] [summary] [slides] ai4sciencedggraphs
  19. Martinez-Rubio, D., Roux, C., and Pokutta, S. (2024). Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point. Proceedings of ICML. [arXiv] mlopt
  20. Hendrych, D., Besançon, M., and Pokutta, S. (2024). Solving the Optimal Experiment Design Problem with Mixed-Integer Convex Methods. Proceedings of Symposium on Experimental Algorithms (SEA). [PDF] [arXiv] [code] ipopt
  21. Mundinger, K., Zimmer, M., and Pokutta, S. (2024). Neural Parameter Regression for Explicit Representations of PDE Solution Operators. ICLR 2024 Workshop on AI4DifferentialEquations In Science. [PDF] [arXiv] [slides] [poster] ai4scienceml
  22. Zimmer, M., Spiegel, C., and Pokutta, S. (2024). Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging. Proceedings of ICLR. [arXiv] mlsparsity
  23. Wäldchen, S., Sharma, K., Turan, B., Zimmer, M., and Pokutta, S. (2024). Interpretability Guarantees with Merlin-Arthur Classifiers. Proceedings of AISTATS. [arXiv] mlxai
  24. Thuerck, D., Sofranac, B., Pfetsch, M., and Pokutta, S. (2023). Learning Cuts via Enumeration Oracles. Proceedings of NeurIPS. [arXiv] ipmlopt
  25. Martinez-Rubio, D., Roux, C., Criscitiello, C., and Pokutta, S. (2023). Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. NeurIPS OPT 2023 Workshop. [arXiv] mlopt
  26. Martinez-Rubio, D., Wirth, E., and Pokutta, S. (2023). Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. Proceedings of COLT. [arXiv] [slides] [poster] opt
  27. Martinez-Rubio, D., and Pokutta, S. (2023). Accelerated Riemannian Optimization: Handling Constraints to Bound Geometric Penalties. Proceedings of COLT. [arXiv] [poster] mlopt
  28. Wirth, E., Kera, H., and Pokutta, S. (2023). Approximate Vanishing Ideal Computations at Scale. Proceedings of ICLR. [arXiv] [slides] [poster] compalgmlopt
  29. Zimmer, M., Spiegel, C., and Pokutta, S. (2023). How I Learned to Stop Worrying and Love Retraining. Proceedings of ICLR. [arXiv] [poster] [code] mlsparsity
  30. Chmiela, A., Gleixner, A., Lichocki, P., and Pokutta, S. (2023). Online Learning for Scheduling MIP Heuristics. Proceedings of CPAIOR. [arXiv] ipmlopt
  31. Wirth, E., Kerdreux, T., and Pokutta, S. (2023). Acceleration of Frank-Wolfe algorithms with open loop step-sizes. Proceedings of AISTATS. [arXiv] [poster] mlopt
  32. Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2022). Fully Computer-Assisted Proofs in Extremal Combinatorics. Proceedings of AAAI. [arXiv] [slides] ai4sciencecombinatoricsgraphs
  33. Martinez-Rubio, D., and Pokutta, S. (2022). Accelerated Riemannian Optimization: Handling Constraints to Bound Geometric Penalties. NeurIPS OPT 2022 Workshop. [arXiv] [poster] mlopt
  34. Criado, F., Martinez-Rubio, D., and Pokutta, S. (2022). Fast Algorithms for Packing Proportional Fairness and its Dual. Proceedings of NeurIPS. [PDF] [arXiv] [poster] [video] mlopt
  35. Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2022). New Ramsey Multiplicity Bounds and Search Heuristics. Proceedings of Discrete Mathematics Days. [arXiv] [slides] [code] ai4sciencecombinatorics
  36. Macdonald, J., Besançon, M., and Pokutta, S. (2022). Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. Proceedings of ICML. [arXiv] [poster] [video] mlxai
  37. Wäldchen, S., Huber, F., and Pokutta, S. (2022). Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four. Proceedings of ICML. [arXiv] [poster] [video] mlxai (Oral Presentation + Conference Proceedings)
  38. Tsuji, K., Tanaka, K., and Pokutta, S. (2022). Pairwise Conditional Gradients without Swap Steps and Sparser Kernel Herding. Proceedings of ICML. [arXiv] [summary] [slides] [code] [video] mlopt
  39. Gasse, M., Cappart, Q., Charfreitag, J., Charlin, L., Chételat, D., Chmiela, A., Dumouchelle, J., Gleixner, A., Kazachkov, A. M., Khalil, E., Lichocki, P., Lodi, A., Lubin, M., Maddison, C. J., Morris, C., Papageorgiou, D. J., Parjadis, A., Pokutta, S., Prouvost, A., … Kun, M. (2022). The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. Proceedings of Machine Learning Research, 176, 220–231. [arXiv] mlopt
  40. Wirth, E., and Pokutta, S. (2022). Conditional Gradients for the Approximate Vanishing Ideal. Proceedings of AISTATS. [arXiv] [summary] [poster] [code] compalgmlopt
  41. Sofranac, B., Gleixner, A., and Pokutta, S. (2021). An Algorithm-Independent Measure of Progress for Linear Constraint Propagation. Proceedings of International Conference on Principles and Practice of Constraint Programming. [arXiv] [video] hpcipopt
  42. Carderera, A., Besançon, M., and Pokutta, S. (2021). Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions. Proceedings of NeurIPS. [arXiv] [summary] [slides] [poster] [code] mlopt
  43. Chmiela, A., Khalil, E., Gleixner, A., Lodi, A., and Pokutta, S. (2021). Learning to Schedule Heuristics in Branch-and-Bound. Proceedings of NeurIPS. [arXiv] [summary] [poster] ipopt
  44. Carderera, A., Diakonikolas, J., Lin, C. Y., and Pokutta, S. (2021). Parameter-free Locally Accelerated Conditional Gradients. Proceedings of ICML. [arXiv] [slides] mlopt
  45. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021). Projection-Free Optimization on Uniformly Convex Sets. Proceedings of AISTATS. [arXiv] [summary] [slides] mlopt (Oral Presentation + Conference Proceedings)
  46. Pokutta, S. (2020). Restarting Algorithms: Sometimes there is Free Lunch. Proceedings of CPAIOR. [arXiv] [slides] [video] ipopt (Invited Paper)
  47. Sofranac, B., Gleixner, A., and Pokutta, S. (2020). Accelerating Domain Propagation: an Efficient GPU-Parallel Algorithm over Sparse Matrices. Proceedings of IA3 at SC20. [arXiv] [summary] [slides] [video] hpcipopt
  48. Mortagy, H., Gupta, S., and Pokutta, S. (2020). Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization. Proceedings of NeurIPS. [arXiv] [slides] [poster] [code] [video] mlopt
  49. Combettes, C. W., and Pokutta, S. (2020). Boosting Frank-Wolfe by Chasing Gradients. Proceedings of ICML. [PDF] [arXiv] [summary] [slides] [code] [video] mlopt
  50. Pokutta, S., Singh, M., and Torrico, A. (2020). On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness. Proceedings of ICML. [arXiv] [summary] [slides] [poster] [video] ipmlopt
  51. Diakonikolas, J., Carderera, A., and Pokutta, S. (2020). Locally Accelerated Conditional Gradients. Proceedings of AISTATS. [PDF] [arXiv] [summary] [slides] [code] [video] mlopt
  52. Pfetsch, M., and Pokutta, S. (2020). IPBoost – Non-Convex Boosting via Integer Programming. Proceedings of ICML. [arXiv] [summary] [slides] [code] ipmlopt
  53. Combettes, C. W., and Pokutta, S. (2019). Blended Matching Pursuit. Proceedings of NeurIPS. [PDF] [arXiv] [summary] [slides] [poster] [code] mlopt
  54. Pokutta, S., Singh, M., and Torrico, A. (2019). On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness. OPTML Workshop Paper. [PDF] [summary] [poster] ipopt
  55. Diakonikolas, J., Carderera, A., and Pokutta, S. (2019). Breaking the Curse of Dimensionality (Locally) to Accelerate Conditional Gradients. OPTML Workshop Paper. [PDF] [arXiv] [summary] [slides] [poster] [code] mlopt
  56. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2019). Restarting Frank-Wolfe. Proceedings of AISTATS. [PDF] [arXiv] [summary] [slides] mlopt
  57. Braun, G., Pokutta, S., Tu, D., and Wright, S. (2019). Blended Conditional Gradients: the unconditioning of conditional gradients. Proceedings of ICML. [PDF] [arXiv] [summary] [slides] [poster] [code] mlopt
  58. Anari, N., Haghtalab, N., Naor, S., Pokutta, S., Singh, M., and Torrico, A. (2019). Structured Robust Submodular Maximization: Offline and Online Algorithms. Proceedings of AISTATS. [PDF] [arXiv] optrobopt
  59. Inanlouganji, A., Pedrielli, G., Fainekos, G., and Pokutta, S. (2018). Continuous Simulation Optimization with Model Mismatch Using Gaussian Process Regression. Proceedings of the 2018 Winter Simulation Conference. optsimulation
  60. Pokutta, S., Singh, M., and Torrico, A. (2018). Efficient algorithms for robust submodular maximization under matroid constraints. ICML Workshop Paper. [PDF] [arXiv] mloptrobopt
  61. Bärmann, A., Pokutta, S., and Schneider, O. (2017). Emulating the Expert: Inverse Optimization through Online Learning. Proceedings of the International Conference on Machine Learning (ICML). [PDF] [arXiv] [summary] [slides] [poster] [video] ipmlopt
  62. Arumugam, K., Kadampot, I., Tahmasbi, M., Shah, S., Bloch, M., and Pokutta, S. (2017). Modulation Recognition Using Side Information and Hybrid Learning. Proceedings of IEEE DySPAN. mloptsignalprocessing
  63. Roy, A., Xu, H., and Pokutta, S. (2017). Reinforcement Learning under Model Mismatch. Proceedings of NIPS. [arXiv] mlopt
  64. Braun, G., Pokutta, S., and Zink, D. (2017). Lazifying Conditional Gradient Algorithms. Proceedings of the International Conference on Machine Learning (ICML). [PDF] [arXiv] [slides] [poster] mlopt
  65. Lan, G., Pokutta, S., Zhou, Y., and Zink, D. (2017). Conditional Accelerated Lazy Stochastic Gradient Descent. Proceedings of the International Conference on Machine Learning (ICML). [PDF] [arXiv] [poster] mlopt
  66. Roy, A., and Pokutta, S. (2016). Hierarchical Clustering via Spreading Metrics. Proceedings of NIPS. [PDF] [arXiv] mlopt (Oral Presentation + Conference Proceedings)
  67. Braun, G., Roy, A., and Pokutta, S. (2016). Stronger Reductions for Extended Formulations. Proceedings of IPCO. [arXiv] extendedformulationipopt
  68. Braun, G., Brown-Cohen, J., Huq, A., Pokutta, S., Raghavendra, P., Roy, A., Weitz, B., and Zink, D. (2016). The matching problem has no small symmetric SDP. Proceedings of SODA 2016. [PDF] [arXiv] extendedformulationipopt
  69. Song, R., Xie, Y., and Pokutta, S. (2015). Sequential Sensing with Model Mismatch. Proceedings of ISIT. mloptsignalprocessing
  70. Braun, G., and Pokutta, S. (2015). The matching polytope does not admit fully-polynomial size relaxation schemes. Proceeedings of SODA. [arXiv] extendedformulationipopt
  71. Bazzi, A., Fiorini, S., Pokutta, S., and Svensson, O. (2015). Small linear programs cannot approximate Vertex Cover within a factor of 2 - epsilon. Proceedings of FOCS. [arXiv] [slides] extendedformulationipopt
  72. Pokutta, S. (2015). Information Theory and Polyhedral Combinatorics. Proceedings of 53rd Annual Allerton Conference on Communication, Control, and Computing. [PDF] extendedformulationinformationtheoryipoptsurvey
  73. Xie, Y., Li, Q., and Pokutta, S. (2015). Supervised Online Subspace Tracking. Proceedings of Asilomar Conference on Signals, Systems, and Computers. mloptsignalprocessing
  74. Braun, G., Pokutta, S., and Zink, D. (2015). Inapproximability of combinatorial problems via small LPs and SDPs. Proceeedings of STOC. [arXiv] [video] extendedformulationipopt
  75. Braun, G., Pokutta, S., and Xie, Y. (2014). Info-Greedy Sequential Adaptive Compressed Sensing. Proceedings of 52nd Annual Allerton Conference on Communication, Control, and Computing. [arXiv] mloptsignalprocessing
  76. Braun, G., Fiorini, S., and Pokutta, S. (2014). Average case polyhedral complexity of the maximum stable set problem. Proceedings of RANDOM. [PDF] [arXiv] extendedformulationipopt
  77. Schmaltz, C., Pokutta, S., Heidorn, T., and Andrae, S. (2013). How to make regulators and shareholders happy under Basel III. Proceedings of the 26th Australasian Finance and Banking Conference. [arXiv] financeopt
  78. Braun, G., and Pokutta, S. (2013). Common information and unique disjointness. Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium, 688–697. [arXiv] extendedformulationinformationtheoryipopt
  79. Briët, J., Dadush, D., and Pokutta, S. (2013). On the existence of 0/1 polytopes with high semidefinite extension complexity. Proceedings of ESA. [arXiv] extendedformulationipopt
  80. Braun, G., and Pokutta, S. (2012). An algebraic view on symmetric extended formulations. Proceedings of ISCO, Lecture Notes in Computer Science, 7422(141–152). [arXiv] algebraextendedformulationipopt
  81. Fiorini, S., Massar, S., Pokutta, S., Tiwary, H. R., and de Wolf, R. (2012). Linear vs. Semidefinite Extended Formulations: Exponential Separation and Strong Lower Bounds. Proceedings of STOC. [arXiv] extendedformulationipopt (Best paper award at STOC 2012)
  82. Braun, G., Fiorini, S., Pokutta, S., and Steurer, D. (2012). Approximation Limits of Linear Programs (Beyond Hierarchies). Proceedings of FOCS. [arXiv] extendedformulationipopt
  83. Dey, S. S., and Pokutta, S. (2011). Design and verify: a new scheme for generating cutting-planes. Proceedings of IPCO, Lecture Notes in Computer Science, 6655, 143–155. [arXiv] ipopt
  84. Pokutta, S., and Schmaltz, C. (2011). A network model for bank lending capacity. Proceedings of Systemic Risk, Basel III, Financial Stability and Regulation. [arXiv] financeopt
  85. Pokutta, S., and Schmaltz, C. (2011). Optimal Planning under Basel III Regulations. Proceedings of 24th Australasian Finance and Banking Conference. [arXiv] financeopt
  86. Helmke, H., Gluchshenko, O., Martin, A., Peter, A., Pokutta, S., and Siebert, U. (2011). Optimal Mixed-Mode Runway Scheduling. Proceedings of DACS. [arXiv] ipopttranslog
  87. Braun, G., and Pokutta, S. (2010). Rank of random half-integral polytopes. Electronic Notes in Discrete Mathematics, 36, 415–422. [PDF] [arXiv] ipoptprobability
  88. Martin, A., Müller, J., and Pokutta, S. (2010). On clearing coupled day-ahead electricity markets. Proceedings of 23rd Australasian Finance and Banking Conference. [arXiv] energyfinanceopt
  89. Drewes, S., and Pokutta, S. (2010). Cutting-planes for weakly-coupled 0/1 second order cone programs. Electronic Notes in Discrete Mathematics, 36, 735–742. [PDF] [arXiv] ipopt
  90. Drewes, S., and Pokutta, S. (2010). Geometric mean maximization in the presence of discrete decisions. Proceedings of 23rd Australasian Finance and Banking Conference. financeipopt
  91. Pokutta, S., and Schulz, A. S. (2010). On the rank of generic cutting-plane proof systems. Proceedings of IPCO, Lecture Notes in Computer Science, 6080, 450–463. [PDF] [arXiv] ipopt
  92. Pokutta, S., and Schmaltz, C. (2009). Optimal degree of centralization of liquidity management. Proceedings of 22nd Australasian Finance and Banking Conference. [arXiv] financeopt

Refereed Journals ⏵

  1. Wirth, E., Peña, J., and Pokutta, S. (2025). Fast Convergence of Frank-Wolfe algorithms on polytopes. To Appear in Mathematics of Operations Research. [arXiv] mlopt
  2. Hendrych, D., Troppens, H., Besançon, M., and Pokutta, S. (2025). Convex mixed-integer optimization with Frank-Wolfe methods. To Appear in Mathematical Programming Computation. [arXiv] [slides] [poster] [code] ipopt
  3. 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. To Appear in Journal of Computational and Applied Mathematics. [PDF] [arXiv] [summary] ai4sciencemlopt
  4. Vu-Han, T.-L., Sunkara, V., Bermudez-Schettino, R., Schwechten, J., Runge, R., Perka, C., Winkler, T., Pokutta, S., Weiß, C., and Pumberger, M. (2025). Feature Engineering for the Prediction of Scoliosis in 5q-Spinal Muscular Atrophy. Journal of Cachexia, Sarcopenia and Muscle, 16(1). [PDF] ai4sciencemedicineml
  5. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2025). Local and Global Uniform Convexity Conditions. To Appear in Special Issue of Fields Institute Communications. [arXiv] mlopt
  6. Carderera, A., and Pokutta, S. (2025). Second-order Conditional Gradient Sliding. To Appear in Special Issue of Fields Institute Communications. [arXiv] [summary] [code] mlopt
  7. Wirth, E., Peña, J., and Pokutta, S. (2024). Accelerated Affine-Invariant Convergence Rates of the Frank-Wolfe Algorithm with Open-Loop Step-Sizes. To Appear in Mathematical Programming A. [PDF] [arXiv] mlopt
  8. Woodstock, Z., and Pokutta, S. (2024). Splitting the Conditional Gradient Algorithm. To Appear in SIAM Journal on Optimization. [arXiv] mlopt
  9. Abbas, A., Ambainis, A., Augustino, B., Bärtschi, A., Buhrman, H., Coffrin, C., Cortiana, G., Dunjko, V., Egger, D. J., Elmegreen, B. G., Franco, N., Fratini, F., Fuller, B., Gacon, J., Gonciulea, C., Gribling, S., Gupta, S., Hadfield, S., Heese, R., … Zoufal, C. (2024). Quantum Optimization: Potential, Challenges, and the Path Forward. Nature Reviews Physics. [PDF] [arXiv] optphysicsquantumsurvey
  10. Stengl, M., Gelß, P., Klus, S., and Pokutta, S. (2024). Existence and Uniqueness of Solutions of the Koopman–von Neumann Equation on Bounded Domains. To Appear in Journal of Physics A. [arXiv] physics
  11. Deza, A., Onn, S., Pokutta, S., and Pournin, L. (2024). Kissing polytopes. To Appear in SIAM Journal on Discrete Mathematics. [PDF] [arXiv] dm
  12. Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2024). New Ramsey Multiplicity Bounds and Search Heuristics. Foundations of Computational Mathematics. [PDF] [arXiv] [slides] [code] ai4sciencecombinatoricsgraphs
  13. Mundinger, K., Pokutta, S., Spiegel, C., and Zimmer, M. (2024). Extending the Continuum of Six-Colorings. Geombinatorics Quarterly. [arXiv] [summary] [slides] ai4sciencedggraphs
  14. 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). [PDF] [arXiv] [summary] [slides] [poster] [code] mlopt
  15. Designolle, S., Vértesi, T., and Pokutta, S. (2024). Symmetric multipartite Bell inequalities via Frank-Wolfe algorithms. Physical Review A, 109(2). [PDF] [arXiv] optphysicsquantumsymmetry
  16. Deza, A., Pokutta, S., and Pournin, L. (2024). The complexity of geometric scaling. Operations Research Letters, 52. [PDF] [arXiv] dmopt
  17. Pokutta, S. (2024). The Frank-Wolfe algorithm: a short introduction. Jahresbericht Der Deutschen Mathematiker-Vereinigung, 126, 3–35. [PDF] [arXiv] mlopt
  18. Kreimeier, T., Pokutta, S., Walther, A., and Woodstock, Z. (2023). On a Frank-Wolfe Approach for Abs-smooth Functions. To Appear in Optimization Methods and Software. [arXiv] [poster] mlopt
  19. Designolle, S., Iommazzo, G., Besançon, M., Knebel, S., Gelß, P., and Pokutta, S. (2023). Improved local models and new Bell inequalities via Frank-Wolfe algorithms. Physical Reviews Research. [PDF] [arXiv] [slides] optphysicsquantum
  20. Bienstock, D., Muñoz, G., and Pokutta, S. (2023). Principled Deep Neural Network Training through Linear Programming. Discrete Optimization, 49. [PDF] [arXiv] [summary] mlopt
  21. Aigner, K., Bärmann, A., Braun, K., Liers, F., Pokutta, S., Schneider, O., Sharma, K., and Tschuppik, S. (2023). Data-driven Distributionally Robust Optimization over Time. INFORMS Journal on Optimization, 5(4). [PDF] [arXiv] ipmloptrobopt
  22. 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). [PDF] [arXiv] opt
  23. Combettes, C. W., and Pokutta, S. (2023). Revisiting the Approximate Carathéodory Problem via the Frank-Wolfe Algorithm. Mathematical Programming A, 197, 191—214. [PDF] [arXiv] [summary] [slides] [code] [video] opt
  24. Sofranac, B., Gleixner, A., and Pokutta, S. (2022). Accelerating Domain Propagation: an Efficient GPU-Parallel Algorithm over Sparse Matrices. Parallel Computing, 109. [PDF] [arXiv] [summary] [slides] [video] hpcipopt
  25. Sofranac, B., Gleixner, A., and Pokutta, S. (2022). An Algorithm-Independent Measure of Progress for Linear Constraint Propagation. Constraints, 27, 432–455. [PDF] [arXiv] [video] hpcipopt
  26. Kossen, T., Hirzel, M. A., Madai, V. I., Boenisch, F., Hennemuth, A., Hildebrand, K., Pokutta, S., Sharma, K., Hilbert, A., Sobesky, J., Galinovic, I., Khalil, A. A., Fiebach, J. B., and Frey, D. (2022). Towards sharing brain images: Differentially private TOF-MRA images with segmentation labels using generative adversarial networks. Frontiers in Artificial Intelligence. [PDF] ai4sciencemedicineml
  27. 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. [PDF] [arXiv] [summary] [slides] [code] mlopt
  28. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2022). Restarting Frank-Wolfe: Faster Rates under Hölderian Error Bounds. Journal of Optimization Theory and Applications, 192, 799–829. [PDF] [arXiv] [summary] [slides] mlopt
  29. Faenza, Y., Muñoz, G., and Pokutta, S. (2022). New Limits of Treewidth-based tractability in Optimization. Mathematical Programming A, 191, 559–594. [PDF] [arXiv] [summary] extendedformulationipopt
  30. Combettes, C. W., and Pokutta, S. (2021). Complexity of Linear Minimization and Projection on Some Sets. Operations Research Letters, 49(4). [arXiv] [code] opt
  31. Kerdreux, T., Roux, C., d’Aspremont, A., and Pokutta, S. (2021). Linear Bandits on Uniformly Convex Sets. Journal of Machine Learning Research (JMLR), 22(284), 1–23. [PDF] [arXiv] [summary] mlopt
  32. Anari, N., Haghtalab, N., Naor, S., Pokutta, S., Singh, M., and Torrico, A. (2021). Structured Robust Submodular Maximization: Offline and Online Algorithms. INFORMS Journal on Computing, 33(4), 1259–1684. [PDF] [arXiv] optrobopt
  33. Gatzert, N., Pokutta, S., and Vogl, N. (2019). Convergence of Capital and Insurance Markets: Pricing Aspects of Index-Linked Catastrophic Loss Instruments. Journal of Risk and Insurance, 86, 39–72. [arXiv] finance (Best paper award at European Group of Risk and Insurance Economists Annual Meeting 2014 for conference version)
  34. Bazzi, A., Fiorini, S., Pokutta, S., and Svensson, O. (2019). Small linear programs cannot approximate Vertex Cover within a factor of 2 - epsilon. Mathematics of Operations Research, 44(1), 1–375. [arXiv] [slides] extendedformulationipopt
  35. Braun, G., Pokutta, S., and Zink, D. (2019). Lazifying Conditional Gradient Algorithms. Journal of Machine Learning Research (JMLR), 20(71), 1–42. [PDF] [arXiv] [slides] mlopt
  36. Braun, G., Pokutta, S., and Zink, D. (2019). Affine Reductions for LPs and SDPs. Mathematical Programming A, 173(1), 281–312. [PDF] [arXiv] extendedformulationipopt
  37. Knueven, B., Ostrowski, J., and Pokutta, S. (2018). Detecting Almost Symmetries in Graphs. Mathematical Programming C, 10, 143–185. [PDF] [arXiv] graphsipoptsymmetry
  38. Song, R., Xie, Y., and Pokutta, S. (2018). On the effect of model mismatch for sequential Info-Greedy Sensing. EURASIP Journal on Advances in Signal Processing. [PDF] informationtheorymloptsignalprocessing
  39. Bodur, M., Del Pia, A., Dey, S. S., Molinaro, M., and Pokutta, S. (2018). Aggregation-based cutting-planes for packing and covering Integer Programs. Mathematical Programming A, 171, 331–359. [PDF] [arXiv] ipopt
  40. Braun, G., Roy, A., and Pokutta, S. (2018). Stronger Reductions for Extended Formulations. Mathematical Programming B, 172, 591–620. [arXiv] extendedformulationipopt
  41. Le Bodic, P., Pfetsch, M., Pavelka, J., and Pokutta, S. (2018). Solving MIPs via Scaling-based Augmentation. Discrete Optimization, 27, 1–25. [PDF] [arXiv] ipopt
  42. Christensen, H., Khan, A., Pokutta, S., and Tetali, P. (2017). Approximation and online algorithms for multidimensional bin packing: A survey. Computer Science Review, 24, 63–79. [PDF] ipoptsurvey
  43. Roy, A., and Pokutta, S. (2017). Hierarchical Clustering via Spreading Metrics. Journal of Machine Learning Research (JMLR), 18, 1–35. [PDF] [arXiv] mlopt
  44. Braun, G., Brown-Cohen, J., Huq, A., Pokutta, S., Raghavendra, P., Roy, A., Weitz, B., and Zink, D. (2017). The matching problem has no small symmetric SDP. Mathematical Programming A, 165(2), 643–662. [PDF] [arXiv] extendedformulationipoptsymmetry
  45. Braun, G., Guzmán, C., and Pokutta, S. (2017). Unifying Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization. IEEE Transactions of Information Theory, 63(7), 4709–4724. [PDF] [arXiv] informationtheoryopt
  46. Braun, G., Jain, R., Lee, T., and Pokutta, S. (2017). Information-theoretic approximations of the nonnegative rank. Computational Complexity, 26(1), 147–197. [arXiv] extendedformulationinformationtheory
  47. Martin, A., Müller, J., Pape, S., Peter, A., Pokutta, S., and Winter, T. (2017). Pricing and clearing combinatorial markets with singleton and swap orders. Mathematical Methods of Operations Research, 85(2), 155–177. [arXiv] financeipopt
  48. Bärmann, A., Heidt, A., Martin, A., Pokutta, S., and Thurner, C. (2016). Polyhedral Approximation of Ellipsoidal Uncertainty Sets via Extended Formulations - a computational case study. Computational Management Science, 13(2), 151–193. [PDF] [arXiv] extendedformulationipopt
  49. Braun, G., Fiorini, S., and Pokutta, S. (2016). Average case polyhedral complexity of the maximum stable set problem. Mathematical Programming A, 160(1), 407–431. [PDF] [arXiv] extendedformulationipopt
  50. Braun, G., and Pokutta, S. (2016). Common information and unique disjointness. Algorithmica, 76(3), 597–629. [PDF] [arXiv] extendedformulationinformationtheoryipopt (Invited to Special Issue of Algorithmica)
  51. Braun, G., and Pokutta, S. (2016). A polyhedral characterization of Border Bases. SIAM Journal on Discrete Mathematics, 30(1), 239–265. [arXiv] algebraipopt
  52. Briët, J., Dadush, D., and Pokutta, S. (2015). On the existence of 0/1 polytopes with high semidefinite extension complexity. Mathematical Programming B, 153(1), 179–199. [arXiv] extendedformulationipopt
  53. Braun, G., and Pokutta, S. (2015). The matching polytope does not admit fully-polynomial size relaxation schemes. IEEE Transactions on Information Theory, 61(10), 1–11. [PDF] [arXiv] extendedformulationinformationtheoryipopt
  54. Braun, G., Fiorini, S., Pokutta, S., and Steurer, D. (2015). Approximation Limits of Linear Programs (Beyond Hierarchies). Mathematics of Operations Research, 40(3), 179–199. [arXiv] extendedformulationipopt
  55. Fiorini, S., Massar, S., Pokutta, S., Tiwary, H. R., and de Wolf, R. (2015). Exponential Lower Bounds for Polytopes in Combinatorial Optimization. Journal of the ACM, 62(2), 1–17. [PDF] [arXiv] extendedformulationipopt
  56. 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. [arXiv] informationtheoryoptsignalprocessing
  57. Schmaltz, C., Pokutta, S., Heidorn, T., and Andrae, S. (2014). How to make regulators and shareholders happy under Basel III. Journal of Banking and Finance, 311–325. [PDF] [arXiv] financeopt
  58. Martin, A., Müller, J., and Pokutta, S. (2014). Strict linear prices in non-convex European day-ahead electricity markets. Optimization Methods and Software, 29(1), 189–221. [PDF] [arXiv] energyfinanceopt (Best paper award at Energy Finance 2010 for conference version)
  59. Dey, S. S., and Pokutta, S. (2014). Design and verify: a new scheme for generating cutting-planes. Mathematical Programming A, 145, 199–222. [arXiv] ipopt
  60. Drewes, S., and Pokutta, S. (2014). Symmetry-exploiting cuts for a class of mixed-0/1 second order cone programs. Discrete Optimization, 13, 23–35. [arXiv] ipoptsymmetry
  61. Drewes, S., and Pokutta, S. (2014). Computing discrete expected utility maximizing portfolios. Journal of Investing, 23(4), 121–132. [arXiv] financeipopt
  62. Braun, G., and Pokutta, S. (2014). A short proof for the polyhedrality of the Chvátal-Gomory closure of a compact convex set. Operations Research Letters, 42, 307–310. [arXiv] ipopt
  63. Kroll, C., and Pokutta, S. (2013). Just a perfect day: developing a happiness optimised day schedule. Journal of Economic Psychology, 34, 210–217. [PDF] [video] optsocial
  64. Pokutta, S., and Van Vyve, M. (2013). A note on the extension complexity of the knapsack polytope. Operations Research Letters, 41, 347–350. [PDF] [arXiv] ipopt
  65. Braun, G., and Pokutta, S. (2012). Rigid abelian groups and the probabilistic method. Contemporary Mathematics, 576, 17–30. [PDF] [arXiv] algebraprobability
  66. Göbel, R., and Pokutta, S. (2012). Absolutely rigid fields and Shelah’s absolutely rigid trees. Contemporary Mathematics, 576, 105–128. [PDF] [arXiv] algebrasettheory
  67. Pokutta, S., and Schmaltz, C. (2012). Optimal Planning under Basel III Regulations. Cass-Capco Institute Paper Series on Risk, 34. [PDF] [arXiv] financeopt
  68. Braun, G., and Pokutta, S. (2011). Random half-integral polytopes. Operations Research Letters, 39(3), 204–207. [arXiv] ipoptprobability
  69. Haus, U. U., Hemmecke, R., and Pokutta, S. (2011). Reconstructing biochemical cluster networks. Journal of Mathematical Chemistry, 49(10), 2441–2456. [PDF] [arXiv] algebrabiochemistry
  70. Letchford, A. N., Pokutta, S., and Schulz, A. S. (2011). On the membership problem for the 0,1/2-closure. Operations Research Letters, 39(5), 301–304. [PDF] [arXiv] ipopt
  71. Pokutta, S., and Schmaltz, C. (2011). Managing liquidity: Optimal degree of centralization. Journal of Banking and Finance, 35, 627–638. [PDF] [arXiv] financeopt (Cited by the Committee on the Global Financial System in CGFS Papers No 39)
  72. Pokutta, S., and Schulz, A. S. (2011). Integer-empty polytopes in the 0/1-cube with maximal Gomory-Chvátal rank. Operations Research Letters, 39(6), 457–460. [PDF] [arXiv] ipopt
  73. Pokutta, S., and Stauffer, G. (2011). Lower bounds for the Chvátal-Gomory rank in the 0/1 cube. Operations Research Letters, 39(3), 200–203. [PDF] [arXiv] ipopt
  74. Pokutta, S., and Stauffer, G. (2009). France Telecom Workforce Scheduling Problem: a challenge. RAIRO-Operations Research, 43, 375–386. [PDF] ipopt
  75. Heldt, D., Kreuzer, M., Pokutta, S., and Poulisse, H. (2009). Approximate Computation of zero-dimensional polynomial ideals. Journal of Symbolic Computation, 44, 1566–1591. [PDF] algebracompalg
  76. Droste, M., Göbel, R., and Pokutta, S. (2008). Absolute graphs with prescribed endomorphism monoid. Semigroup Forum, 76, 256–267. [PDF] algebragraphssettheory
  77. Göbel, R., and Pokutta, S. (2008). Construction of dual modules using Martin’s axiom. Journal of Algebra, 320, 2388–2404. [PDF] algebrasettheory
  78. Pokutta, S., and Strüngmann, L. (2007). The Chase radical and reduced products. Journal of Pure and Applied Algebra, 211, 532–540. [PDF] algebrasettheory

Unpublished Manuscripts ⏵

  1. Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-Bandit Strategies for Minimax Learning Problems. Preprint. [arXiv] mlopt
  2. Pokutta, S., and Xu, H. (2021). Adversaries in Online Learning Revisited: with applications in Robust Optimization and Adversarial training. Preprint. [arXiv] mloptrobopt
  3. Braun, G., and Pokutta, S. (2021). Dual Prices for Frank-Wolfe Algorithms. Preprint. [arXiv] opt
  4. Braun, G., and Pokutta, S. (2016). An efficient high-probability algorithm for Linear Bandits. Preprint. [arXiv] mlopt
  5. Braun, G., and Pokutta, S. (2015). An information diffusion Fano inequality. Preprint. [arXiv] informationtheorysignalprocessing
  6. Pokutta, S., and Schulz, A. S. (2013). On the rank of cutting-plane proof systems. Preprint. [arXiv] ipopt
  7. Braun, G., and Pokutta, S. (2012). An algebraic view on symmetric extended formulations. Preprint. [arXiv] algebraextendedformulationipopt
  8. Pokutta, S., Schmaltz, C., and Stiller, S. (2011). Measuring Systemic Risk and Contagion in Financial Networks. Preprint. [arXiv] financeopt
  9. Pokutta, S. (2011). Lower bounds for Chvátal-Gomory style operators. Preprint. [arXiv] ipopt
  10. Pokutta, S., and Schulz, A. S. (2009). On the connection of the Sherali-Adams closure and border bases. Preprint. [arXiv] ipopt
  11. Pokutta, S. (2008). Stowage optimization for inland vessels. Preprint. ipopttranslog

Other ⏵

  1. Pokutta, S. (2021). Mathematik, Machine Learning und Artificial Intelligence. Mitteilungen Der DMV (German). [PDF] optoutreach
  2. Collier, V., Ostrowski, J., and Pokutta, S. (2015). A Symmetric Extended Formulation of the Bin Packing Problem. Proceedings of IIE Annual Conference. extendedformulationipoptsymmetry (Paper won second place in the Undergraduate Research Operations Research competition)
  3. Lee, D., and Pokutta, S. (2015). Toward a Science of Autonomy for Physical Systems: Transportation. Computing Community Consortium White Paper. [PDF] autonomousoutreachtranslog
  4. Heldt, D., Kreuzer, M., Pokutta, S., and Poulisse, H. (2006). Algebraische Modellierung mit Methoden der approximativen Computer Algebra und Anwendungen in der Ölindustrie. OR News, 15–18. compalgoutreach
  5. Alf, M., and Pokutta, S. (2006). How logistics service providers can make use of the real options concept. Symposium Mathematik & Logistik, Bad Honnef 2005, Conference Proceedings. outreachtranslog
  6. Pokutta, S. (2005). Products over countable domains [PhD thesis]. In PhD thesis. University of Duisburg-Essen. algebrasettheory
  7. Pokutta, S., and Törner, G. (2005). Fixpunktminimierung bei Binnenschiffen. OR News, 23, 13–17. ipoptoutreachtranslog
  8. Pokutta, S. (2003). Generalizations of the Chase radical and direct products [Master's thesis]. In Diploma thesis. University of Duisburg-Essen. algebrasettheory