IOL Lab @ TUB/ZIB

Recent Papers

  1. Wirth, E., Kerdreux, T., and Pokutta, S. (2022). Acceleration of Frank-Wolfe algorithms with open loop step-sizes. Preprint.
  2. Kerdreux, T., Scieur, D., d’Aspremont, A., and Pokutta, S. (2022). Strong Convexity of Feasible Sets in Riemannian Manifolds. Preprint.
  3. Wäldchen, S., Huber, F., and Pokutta, S. (2022). Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four. Preprint. [arXiv]
  4. Wirth, E., and Pokutta, S. (2022). Conditional Gradients for the Approximately Vanishing Ideal. To Appear in Proceedings of AISTATS. [arXiv] [summary] [poster] [code]
  5. 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. (2021). Towards sharing brain images: Differentially private TOF-MRA images with segmentation labels using generative adversarial networks. Preprint.
  6. Zimmer, M., Spiegel, C., and Pokutta, S. (2021). How I Learned to Stop Worrying and Love Retraining. Preprint. [arXiv] [code]
  7. Tsuji, K., Tanaka, K., and Pokutta, S. (2021). Sparser Kernel Herding with Pairwise Conditional Gradients without Swap Steps. Preprint. [arXiv] [code]
  8. Macdonald, J., Besançon, M., and Pokutta, S. (2021). Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. Preprint. [arXiv]
  9. Criado, F., Martinez-Rubio, D., and Pokutta, S. (2021). Fast Algorithms for Packing Proportional Fairness and its Dual. Preprint. [arXiv] [poster]
  10. Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-Bandit Strategies for Minimax Learning Problems. Preprint. [arXiv]
  11. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021). Local and Global Uniform Convexity Conditions. Preprint. [arXiv]
  12. Braun, G., and Pokutta, S. (2021). Dual Prices for Frank-Wolfe Algorithms. Preprint. [arXiv]
  13. Pokutta, S., Spiegel, C., and Zimmer, M. (2020). Deep Neural Network Training with Frank-Wolfe. Preprint. [arXiv] [summary] [code]
  14. Combettes, C. W., Spiegel, C., and Pokutta, S. (2020). Projection-Free Adaptive Gradients for Large-Scale Optimization. Preprint. [arXiv] [summary] [code]
  15. Combettes, C. W., and Pokutta, S. (2020). Boosting Frank-Wolfe by Chasing Gradients. Proceedings of ICML. [PDF] [arXiv] [summary] [slides] [code] [video]
  16. Carderera, A., and Pokutta, S. (2020). Second-order Conditional Gradient Sliding. Preprint. [arXiv] [summary] [code]
  17. Pfetsch, M., and Pokutta, S. (2020). IPBoost – Non-Convex Boosting via Integer Programming. To Appear in Proceedings of ICML. [arXiv] [summary] [slides] [code]
  18. Pokutta, S., Singh, M., and Torrico, A. (2020). On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness. To Appear in Proceedings of ICML. [arXiv] [summary] [slides] [poster] [video]

All Publications

In Preparation / Articles Pending Review

  1. Wirth, E., Kerdreux, T., and Pokutta, S. (2022). Acceleration of Frank-Wolfe algorithms with open loop step-sizes. Preprint.
  2. Kerdreux, T., Scieur, D., d’Aspremont, A., and Pokutta, S. (2022). Strong Convexity of Feasible Sets in Riemannian Manifolds. Preprint.
  3. Wäldchen, S., Huber, F., and Pokutta, S. (2022). Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four. Preprint. [arXiv]
  4. 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. (2021). Towards sharing brain images: Differentially private TOF-MRA images with segmentation labels using generative adversarial networks. Preprint.
  5. Zimmer, M., Spiegel, C., and Pokutta, S. (2021). How I Learned to Stop Worrying and Love Retraining. Preprint. [arXiv] [code]
  6. Tsuji, K., Tanaka, K., and Pokutta, S. (2021). Sparser Kernel Herding with Pairwise Conditional Gradients without Swap Steps. Preprint. [arXiv] [code]
  7. Macdonald, J., Besançon, M., and Pokutta, S. (2021). Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings. Preprint. [arXiv]
  8. Criado, F., Martinez-Rubio, D., and Pokutta, S. (2021). Fast Algorithms for Packing Proportional Fairness and its Dual. Preprint. [arXiv] [poster]
  9. Roux, C., Wirth, E., Pokutta, S., and Kerdreux, T. (2021). Efficient Online-Bandit Strategies for Minimax Learning Problems. Preprint. [arXiv]
  10. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021). Local and Global Uniform Convexity Conditions. Preprint. [arXiv]
  11. Carderera, A., Pokutta, S., Schütte, C., and Weiser, M. (2021). CINDy: Conditional gradient-based Identification of Non-linear Dynamics – Noise-robust recovery. Preprint. [arXiv] [summary]
  12. Pokutta, S., Spiegel, C., and Zimmer, M. (2020). Deep Neural Network Training with Frank-Wolfe. Preprint. [arXiv] [summary] [code]
  13. Combettes, C. W., Spiegel, C., and Pokutta, S. (2020). Projection-Free Adaptive Gradients for Large-Scale Optimization. Preprint. [arXiv] [summary] [code]
  14. Carderera, A., and Pokutta, S. (2020). Second-order Conditional Gradient Sliding. Preprint. [arXiv] [summary] [code]
  15. Bärmann, A., Martin, A., Pokutta, S., and Schneider, O. (2018). An Online-Learning Approach to Inverse Optimization. Submitted. [arXiv] [summary] [slides]
  16. Bienstock, D., Muñoz, G., and Pokutta, S. (2018). Principled Deep Neural Network Training through Linear Programming. Submitted. [arXiv] [summary]

Refereed Conference Proceedings

  1. Wirth, E., and Pokutta, S. (2022). Conditional Gradients for the Approximately Vanishing Ideal. To Appear in Proceedings of AISTATS. [arXiv] [summary] [poster] [code]
  2. Sofranac, B., Gleixner, A., and Pokutta, S. (2021). An Algorithm-Independent Measure of Progress for Linear Constraint Propagation. To Appear in Proceedings of International Conference on Principles and Practice of Constraint Programming. [arXiv] [video]
  3. Carderera, A., Besançon, M., and Pokutta, S. (2021). Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions. To Appear in Proceedings of NeurIPS. [arXiv] [slides] [poster] [code]
  4. Chmiela, A., Khalil, E., Gleixner, A., Lodi, A., and Pokutta, S. (2021). Learning to Schedule Heuristics in Branch-and-Bound. To Appear in Proceedings of NeurIPS. [arXiv] [summary] [poster]
  5. Carderera, A., Diakonikolas, J., Lin, C. Y., and Pokutta, S. (2021). Parameter-free Locally Accelerated Conditional Gradients. To Appear in Proceedings of ICML. [arXiv] [slides]
  6. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2021). Projection-Free Optimization on Uniformly Convex Sets. To Appear in Proceedings of AISTATS. [arXiv] [summary] [slides]
  7. Sofranac, B., Gleixner, A., and Pokutta, S. (2020). Accelerating Domain Propagation: an Efficient GPU-Parallel Algorithm over Sparse Matrices. Proceedings of IA^3 at SC20. [arXiv] [summary] [slides] [video]
  8. Pokutta, S. (2020). Restarting Algorithms: Sometimes there is Free Lunch. Proceedings of CPAIOR. [arXiv] [slides] [video]
  9. 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]
  10. Combettes, C. W., and Pokutta, S. (2020). Boosting Frank-Wolfe by Chasing Gradients. Proceedings of ICML. [PDF] [arXiv] [summary] [slides] [code] [video]
  11. Pfetsch, M., and Pokutta, S. (2020). IPBoost – Non-Convex Boosting via Integer Programming. To Appear in Proceedings of ICML. [arXiv] [summary] [slides] [code]
  12. Pokutta, S., Singh, M., and Torrico, A. (2020). On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness. To Appear in Proceedings of ICML. [arXiv] [summary] [slides] [poster] [video]
  13. Diakonikolas, J., Carderera, A., and Pokutta, S. (2020). Locally Accelerated Conditional Gradients. Proceedings of AISTATS. [PDF] [arXiv] [summary] [slides] [code] [video]
  14. 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]
  15. 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]
  16. Combettes, C. W., and Pokutta, S. (2019). Blended Matching Pursuit. Proceedings of NeurIPS. [PDF] [arXiv] [summary] [slides] [poster] [code]
  17. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2019). Restarting Frank-Wolfe. Proceedings of AISTATS. [PDF] [arXiv] [summary] [slides]
  18. 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]
  19. 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]
  20. 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.
  21. Pokutta, S., Singh, M., and Torrico, A. (2018). Efficient algorithms for robust submodular maximization under matroid constraints. ICML Workshop Paper. [PDF] [arXiv]
  22. Roy, A., Xu, H., and Pokutta, S. (2017). Reinforcement Learning under Model Mismatch. Proceedings of NIPS. [arXiv]
  23. 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]
  24. 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]
  25. 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]
  26. 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.
  27. Braun, G., and Strüngmann, L. (2017). Rigid ℵ1-Free Abelian Groups with Prescribed Factors and Their Role in the Theory of Cellular Covers. In M. Droste, L. Fuchs, B. Goldsmith, and L. Strüngmann (Eds.), Groups, Modules, and Model Theory - Surveys and Recent Developments: In Memory of Rüdiger Göbel (pp. 69–81). Springer International Publishing. https://doi.org/10.1007/978-3-319-51718-64
  28. Roy, A., and Pokutta, S. (2016). Hierarchical Clustering via Spreading Metrics. Proceedings of NIPS. [PDF] [arXiv]
  29. Braun, G., Roy, A., and Pokutta, S. (2016). Stronger Reductions for Extended Formulations. Proceedings of IPCO. [arXiv]
  30. Braun, G., Brown-Cohen, J., Huq, A., Pokutta, S., Raghavendra, P., Weitz, B., and Zink, D. (2016). The matching problem has no small symmetric SDP. Proceedings of SODA 2016. [PDF] [arXiv]
  31. Braun, G., Pokutta, S., and Zink, D. (2015). Inapproximability of combinatorial problems via small LPs and SDPs. Proceeedings of STOC. [arXiv] [video]
  32. Pokutta, S. (2015). Information Theory and Polyhedral Combinatorics. Proceedings of 53rd Annual Allerton Conference on Communication, Control, and Computing. [PDF]
  33. Xie, Y., Li, Q., and Pokutta, S. (2015). Supervised Online Subspace Tracking. Proceedings of Asilomar Conference on Signals, Systems, and Computers.
  34. Braun, G., and Pokutta, S. (2015). The matching polytope does not admit fully-polynomial size relaxation schemes. Proceeedings of SODA. [arXiv]
  35. Bazzi, A., Fiorini, S., Pokutta, S., and Svensson, O. (2015). Small linear programs cannot approximate Vertex Cover within a factor of 2 - ε. Proceedings of FOCS. [arXiv] [slides]
  36. Song, R., Xie, Y., and Pokutta, S. (2015). Sequential Sensing with Model Mismatch. Proceedings of ISIT.
  37. 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]
  38. Braun, G., Fiorini, S., and Pokutta, S. (2014). Average case polyhedral complexity of the maximum stable set problem. Proceedings of RANDOM. [PDF] [arXiv]
  39. 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]
  40. Braun, G., and Pokutta, S. (2013). Common information and unique disjointness. Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium, 688–697. [arXiv]
  41. Briët, J., Dadush, D., and Pokutta, S. (2013). On the existence of 0/1 polytopes with high semidefinite extension complexity. Proceedings of ESA. [arXiv]
  42. Braun, G., Fiorini, S., Pokutta, S., and Steurer, D. (2012). Approximation Limits of Linear Programs (Beyond Hierarchies). Proceedings of FOCS. [arXiv]
  43. 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]
  44. 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]
  45. 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]
  46. Helmke, H., Gluchshenko, O., Martin, A., Peter, A., Pokutta, S., and Siebert, U. (2011). Optimal Mixed-Mode Runway Scheduling. Proceedings of DACS. [arXiv]
  47. Pokutta, S., and Schmaltz, C. (2011). A network model for bank lending capacity. Proceedings of Systemic Risk, Basel III, Financial Stability and Regulation. [arXiv]
  48. Pokutta, S., and Schmaltz, C. (2011). Optimal Planning under Basel III Regulations. Proceedings of 24th Australasian Finance and Banking Conference. [arXiv]
  49. Braun, G., and Pokutta, S. (2010). Rank of random half-integral polytopes. Electronic Notes in Discrete Mathematics, 36, 415–422. [PDF] [arXiv]
  50. 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]
  51. Drewes, S., and Pokutta, S. (2010). Geometric mean maximization in the presence of discrete decisions. Proceedings of 23rd Australasian Finance and Banking Conference.
  52. Martin, A., Müller, J., and Pokutta, S. (2010). On clearing coupled day-ahead electricity markets. Proceedings of 23rd Australasian Finance and Banking Conference. [arXiv]
  53. 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]
  54. Pokutta, S., and Schmaltz, C. (2009). Optimal degree of centralization of liquidity management. Proceedings of 22nd Australasian Finance and Banking Conference. [arXiv]

Refereed Journals

  1. 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]
  2. Kerdreux, T., d’Aspremont, A., and Pokutta, S. (2022). Restarting Frank-Wolfe: Faster Rates under Hölderian Error Bounds. Journal of Optimization Theory and Applications. [PDF] [arXiv] [summary] [slides]
  3. Sofranac, B., Gleixner, A., and Pokutta, S. (2021+). Accelerating Domain Propagation: an Efficient GPU-Parallel Algorithm over Sparse Matrices. To Appear in Parallel Computing. [PDF] [arXiv] [summary] [slides] [video]
  4. Combettes, C. W., and Pokutta, S. (2021+). Revisiting the Approximate Carathéodory Problem via the Frank-Wolfe Algorithm. To Appear in Mathematical Programming A. https://doi.org/10.1007/s10107-021-01735-x [PDF] [arXiv] [summary] [slides] [code] [video]
  5. Kerdreux, T., Roux, C., d’Aspremont, A., and Pokutta, S. (2021). Linear Bandits on Uniformly Convex Sets. To Appear in Journal of Machine Learning Research (JMLR). [PDF] [arXiv] [summary]
  6. Combettes, C. W., and Pokutta, S. (2021). Complexity of Linear Minimization and Projection on Some Sets. To Appear in Operations Research Letters. [arXiv] [code]
  7. Faenza, Y., Muñoz, G., and Pokutta, S. (2020+). New Limits of Treewidth-based tractability in Optimization. To Appear in Mathematical Programming A. [PDF] [arXiv] [summary]
  8. Anari, N., Haghtalab, N., Naor, S., Pokutta, S., Singh, M., and Torrico, A. (2020+). Structured Robust Submodular Maximization: Offline and Online Algorithms. To Appear in INFORMS Journal on Computing. [PDF] [arXiv]
  9. 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]
  10. Braun, G., Pokutta, S., and Zink, D. (2019). Affine Reductions for LPs and SDPs. Mathematical Programming A, 173(1), 281–312. [PDF] [arXiv]
  11. Braun, G., Roy, A., and Pokutta, S. (2018+). Stronger Reductions for Extended Formulations. To Appear in Mathematical Programming B. [arXiv]
  12. 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]
  13. Le Bodic, P., Pfetsch, M., Pavelka, J., and Pokutta, S. (2018). Solving MIPs via Scaling-based Augmentation. Discrete Optimization, 27, 1–25. [PDF] [arXiv]
  14. Bazzi, A., Fiorini, S., Pokutta, S., and Svensson, O. (2017+). Small linear programs cannot approximate Vertex Cover within a factor of 2 - ε. To Appear in Mathematics of Operations Research. [arXiv] [slides]
  15. Bodur, M., Del Pia, A., Dey, S. S., Molinaro, M., and Pokutta, S. (2017+). Aggregation-based cutting-planes for packing and covering Integer Programs. To Appear in Mathematical Programming A. [PDF] [arXiv]
  16. Christensen, H., Khan, A., Pokutta, S., and Tetali, P. (2017+). Multidimensional Bin Packing and Other Related Problems: A survey. To Appear in Computer Science Review. [PDF]
  17. Knueven, B., Ostrowski, J., and Pokutta, S. (2017+). Detecting Almost Symmetries in Graphs. To Appear in Mathematical Programming C. [PDF] [arXiv]
  18. Roy, A., and Pokutta, S. (2017). Hierarchical Clustering via Spreading Metrics. Journal of Machine Learning Research (JMLR), 18, 1–35. [PDF] [arXiv]
  19. Braun, G., Brown-Cohen, J., Huq, A., Pokutta, S., Raghavendra, P., Weitz, B., and Zink, D. (2017). The matching problem has no small symmetric SDP. Mathematical Programming A, 165(2), 643–662. [PDF] [arXiv]
  20. 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]
  21. Braun, G., Jain, R., Lee, T., and Pokutta, S. (2017). Information-theoretic approximations of the nonnegative rank. Computational Complexity, 26(1), 147–197. [arXiv]
  22. 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]
  23. Gatzert, N., Pokutta, S., and Vogl, N. (2016+). Convergence of Capital and Insurance Markets: Pricing Aspects of Index-Linked Catastrophic Loss Instruments. To Appear in Journal of Risk and Insurance. [arXiv]
  24. 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]
  25. 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]
  26. Braun, G., and Pokutta, S. (2016). Common information and unique disjointness. Algorithmica, 76(3), 597–629. [PDF] [arXiv]
  27. Braun, G., and Pokutta, S. (2016). A polyhedral characterization of Border Bases. SIAM Journal on Discrete Mathematics, 30(1), 239–265. [arXiv]
  28. Braun, G., and Strüngmann, L. (2016). Examples of non-dual subgroups of the Baer–Specker group. Houston Journal of Mathematics, 42(3), 723–739.
  29. Braun, G., and Strüngmann, L. (2015). The independence of the notions of Hopfian and co-Hopfian Abelian p-groups. Proc. Amer. Math. Soc., 143, 3331–3341. https://doi.org/10.1090/proc/12413
  30. 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]
  31. 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]
  32. 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]
  33. 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]
  34. 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]
  35. 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]
  36. Dey, S. S., and Pokutta, S. (2014). Design and verify: a new scheme for generating cutting-planes. Mathematical Programming A, 145, 199–222. [arXiv]
  37. 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]
  38. Drewes, S., and Pokutta, S. (2014). Computing discrete expected utility maximizing portfolios. Journal of Investing, 23(4), 121–132. [arXiv]
  39. 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]
  40. 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]
  41. 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]
  42. 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]
  43. Braun, G., and Göbel, R. (2012). Splitting kernels into small summands. Israel Journal of Mathematics, 188(1), 221–230. https://doi.org/10.1007/s11856-011-0121-6
  44. Braun, G., and Pokutta, S. (2012). Rigid abelian groups and the probabilistic method. Contemporary Mathematics, 576, 17–30. [PDF] [arXiv]
  45. Göbel, R., and Pokutta, S. (2012). Absolutely rigid fields and Shelah’s absolutely rigid trees. Contemporary Mathematics, 576, 105–128. [PDF] [arXiv]
  46. Pokutta, S., and Schmaltz, C. (2012). Optimal Planning under Basel III Regulations. Cass-Capco Institute Paper Series on Risk, 34. [PDF] [arXiv]
  47. Braun, G., and Trlifaj, J. (2011). Strong submodules of almost projective modules. Pacific Journal of Mathematics, 254(1), 73–87.
  48. Braun, G., and Pokutta, S. (2011). Random half-integral polytopes. Operations Research Letters, 39(3), 204–207. [arXiv]
  49. Haus, U. U., Hemmecke, R., and Pokutta, S. (2011). Reconstructing biochemical cluster networks. Journal of Mathematical Chemistry, 49(10), 2441–2456. [PDF] [arXiv]
  50. 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]
  51. Pokutta, S., and Schmaltz, C. (2011). Managing liquidity: Optimal degree of centralization. Journal of Banking and Finance, 35, 627–638. https://doi.org/DOI: 10.1016/j.jbankfin.2010.07.001 [PDF] [arXiv]
  52. 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]
  53. 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]
  54. Braun, G., and Strüngmann, L. (2011). Breaking up finite automata presentable torsion-free abelian groups. Int. J. Algebra Comput., 21(8), 1463–1472. https://doi.org/10.1142/S0218196711006625
  55. Braun, G., and Némethi, A. (2010). Surgery formulas for Seiberg–Witten invariants of negative definite plumbed 3-manifolds. Journal Für Die Reine Und Angewandte Mathematik, 638, 189–208. https://doi.org/10.1515/CRELLE.2010.007
  56. 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]
  57. Pokutta, S., and Stauffer, G. (2009). France Telecom Workforce Scheduling Problem: a challenge. RAIRO-Operations Research, 43, 375–386. [PDF]
  58. Braun, G. (2008). The cobordism class of the multiple points of immersions. Algebraic & Geometric Topology, 8, 581–601. https://doi.org/10.2140/agt.2008.8.581 [PDF]
  59. Droste, M., Göbel, R., and Pokutta, S. (2008). Absolute graphs with prescribed endomorphism monoid. Semigroup Forum, 76, 256–267. [PDF]
  60. Göbel, R., and Pokutta, S. (2008). Construction of dual modules using Martin’s axiom. Journal of Algebra, 320, 2388–2404. [PDF]
  61. Pokutta, S., and Strüngmann, L. (2007). The Chase radical and reduced products. Journal of Pure and Applied Algebra, 211, 532–540. [PDF]
  62. Braun, G., and Némethi, A. (2007). Invariants of Newton non-degenerate surface singularities. Compositio Mathematica, 143, 1003–1036. [PDF]
  63. Braun, G., and Lippner, G. (2006). Characteristic numbers of multiple-point manifolds. Bull. London Math. Soc., 38, 667–678. https://doi.org/10.1112/S0024609306018571 [PDF]
  64. Braun, G., and Göbel, R. (2005). E-algebras whose torsion part is not cyclic. Proc. Amer. Math. Soc., 133(8), 2251–2258 (electronic). [PDF]
  65. Braun, G. (2005). Characterization of matrix types of ultramatricial algebras. New York J. Math., 11, 21–33. [PDF]
  66. Blass, A., and Braun, G. (2005). Random Orders and Gambler’s Ruin. Electronic Journal of Combinatorics, 12(1), R23. [PDF]
  67. Braun, G. (2004). A proof of Higgins’s conjecture. Bull. Austral. Math. Soc., 70(2), 207–212.
  68. Braun, G., and Göbel, R. (2003). Automorphism groups of nilpotent groups. Arch. Math. (Basel), 80(5), 464–474. https://doi.org/10.1007/s00013-003-0802-4 [PDF]
  69. Braun, G., and Göbel, R. (2003). Outer automorphisms of locally finite p-groups. J. Algebra, 264(1), 55–67.

Unpublished Manuscripts

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

Other

  1. Pokutta, S. (2021). Mathematik, Machine Learning und Artificial Intelligence. To Appear in Mitteilungen Der DMV (German). [PDF]
  2. Lee, D., and Pokutta, S. (2015). Toward a Science of Autonomy for Physical Systems: Transportation. Computing Community Consortium White Paper. [PDF]
  3. 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.
  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.
  5. Pokutta, S. (2005). Products over countable domains [PhD thesis]. University of Duisburg-Essen.
  6. Pokutta, S., and Törner, G. (2005). Fixpunktminimierung bei Binnenschiffen. OR News, 23, 13–17.
  7. Pokutta, S. (2003). Generalizations of the Chase radical and direct products [Master's thesis]. University of Duisburg-Essen.
  8. Braun, G. (2003). Outer automorphism groups [PhD thesis]. Universität Duisburg-Essen. [PDF]