TY - JOUR A1 - Pokutta, Sebastian A1 - Stauffer, G. T1 - France Telecom Workforce Scheduling Problem: a challenge JF - RAIRO-Operations Research Y1 - 2009 N1 - Additional Note: DOI: 10.1051/ro/2009025 N1 - URL of the PDF: http://dx.doi.org/10.1051/ro/2009025 IS - 43 SP - 375 EP - 386 ER - TY - JOUR A1 - Braun, Gábor A1 - Pokutta, Sebastian T1 - Random half-integral polytopes JF - Operations Research Letters Y1 - 2011 UR - http://www.optimization-online.org/DB_HTML/2010/11/2813.html N1 - Additional Note: doi:10.1016/j.orl.2011.03.003 VL - 39 IS - 3 SP - 204 EP - 207 ER - TY - JOUR A1 - Haus, Utz-Uwe A1 - Hemmecke, R. A1 - Pokutta, Sebastian T1 - Reconstructing biochemical cluster networks JF - Journal of Mathematical Chemistry Y1 - 2011 N1 - URL of the PDF: http://dx.doi.org/10.1007/s10910-011-9892-6 VL - 49 IS - 10 SP - 2441 EP - 2456 ER - TY - JOUR A1 - Letchford, A.N. A1 - Pokutta, Sebastian A1 - Schulz, A.S. T1 - On the membership problem for the 0,1/2-closure JF - Operations Research Letters Y1 - 2011 UR - http://www.box.net/shared/zxx88q38l3 N1 - Additional Note: doi:10.1016/j.orl.2011.07.003 N1 - URL of the PDF: http://dx.doi.org/10.1016/j.orl.2011.07.003 VL - 39 IS - 5 SP - 301 EP - 304 ER - TY - JOUR A1 - Pokutta, Sebastian A1 - Schulz, A.S. T1 - Integer-empty polytopes in the 0/1-cube with maximal Gomory-Chvátal rank JF - Operations Research Letters Y1 - 2011 UR - http://www.optimization-online.org/DB_HTML/2010/12/2850.html N1 - Additional Note: doi:10.1016/j.orl.2011.09.004 N1 - URL of the PDF: http://dx.doi.org/10.1016/j.orl.2011.09.004 VL - 39 IS - 6 SP - 457 EP - 460 ER - TY - JOUR A1 - Drewes, S. A1 - Pokutta, Sebastian T1 - Computing discrete expected utility maximizing portfolios JF - Journal of Investing Y1 - 2014 UR - http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1662729 VL - 23 IS - 4 SP - 121 EP - 132 ER - TY - JOUR A1 - Drewes, S. A1 - Pokutta, Sebastian T1 - Symmetry-exploiting cuts for a class of mixed-0/1 second order cone programs JF - Discrete Optimization Y1 - 2014 UR - http://www.optimization-online.org/DB_HTML/2010/06/2652.html N1 - Additional Note: doi:10.1016/j.disopt.2014.04.002 VL - 13 SP - 23 EP - 35 ER - TY - JOUR A1 - Martin, Alexander A1 - Müller, J. A1 - Pokutta, Sebastian T1 - Strict linear prices in non-convex European day-ahead electricity markets JF - Optimization Methods and Software Y1 - 2014 UR - http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1660528 N1 - Additional Note: 10.1080/10556788.2013.823544 N1 - URL of the PDF: http://dx.doi.org/10.1080/10556788.2013.823544 VL - 29 IS - 1 SP - 189 EP - 221 ER - TY - JOUR A1 - Briët, Jop A1 - Dadush, Daniel A1 - Pokutta, Sebastian T1 - On the existence of 0/1 polytopes with high semidefinite extension complexity JF - Mathematical Programming B Y1 - 2015 VL - 153 IS - 1 SP - 179 EP - 199 ER - TY - JOUR A1 - Braun, Gábor A1 - Fiorini, Samuel A1 - Pokutta, Sebastian A1 - Steurer, D. T1 - Approximation Limits of Linear Programs (Beyond Hierarchies) JF - Mathematics of Operations Research Y1 - 2015 VL - 40 IS - 3 SP - 179 EP - 199 ER - TY - JOUR A1 - Braun, Gábor A1 - Pokutta, Sebastian T1 - The matching polytope does not admit fully-polynomial size relaxation schemes JF - IEEE Transactions on Information Theory Y1 - 2015 N1 - Additional Note: doi:10.1109/TIT.2015.2465864 N1 - URL of the PDF: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7182355 VL - 61 IS - 10 SP - 1 EP - 11 ER - TY - JOUR A1 - Braun, Gábor A1 - Pokutta, Sebastian A1 - Xie, Y. T1 - Info-Greedy Sequential Adaptive Compressed Sensing JF - IEEE Journal of Selected Topics in Signal Processing Y1 - 2015 VL - 9 IS - 4 SP - 601 EP - 611 ER - TY - JOUR A1 - Fiorini, Samuel A1 - Massar, S. A1 - Pokutta, Sebastian A1 - Tiwary, H.R. A1 - de Wolf, R. T1 - Exponential Lower Bounds for Polytopes in Combinatorial Optimization JF - Journal of the ACM Y1 - 2015 N1 - URL of the PDF: https://doi.org/10.1145/2716307 VL - 62 IS - 2 SP - 1 EP - 17 ER - TY - JOUR A1 - Braun, Gábor A1 - Fiorini, Samuel A1 - Pokutta, Sebastian T1 - Average case polyhedral complexity of the maximum stable set problem JF - Mathematical Programming A Y1 - 2016 N1 - Additional Note: http://www.dagstuhl.de/dagpub/978-3-939897-74-3 N1 - URL of the PDF: https://rdcu.be/7uOy VL - 160 IS - 1 SP - 407 EP - 431 ER - TY - JOUR A1 - Braun, Gábor A1 - Pokutta, Sebastian T1 - A polyhedral characterization of Border Bases JF - SIAM Journal on Discrete Mathematics Y1 - 2016 VL - 30 IS - 1 SP - 239 EP - 265 ER - TY - CHAP A1 - Pokutta, Sebastian A1 - Schmaltz, Christian T1 - Optimal Planning under Basel III Regulations T2 - Proceedings of 24th Australasian Finance and Banking Conference Y1 - 2011 UR - http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1914800 ER - TY - CHAP A1 - Braun, Gábor A1 - Fiorini, Samuel A1 - Pokutta, Sebastian A1 - Steurer, D. T1 - Approximation Limits of Linear Programs (Beyond Hierarchies) T2 - Proceedings of FOCS Y1 - 2012 ER - TY - CHAP A1 - Braun, Gábor A1 - Pokutta, Sebastian T1 - An algebraic view on symmetric extended formulations T2 - Proceedings of ISCO, Lecture Notes in Computer Science Y1 - 2012 VL - 7422 IS - 141--152 ER - TY - CHAP A1 - Fiorini, Samuel A1 - Massar, S. A1 - Pokutta, Sebastian A1 - Tiwary, H.R. A1 - de Wolf, R. T1 - Linear vs. Semidefinite Extended Formulations: Exponential Separation and Strong Lower Bounds T2 - Proceedings of STOC Y1 - 2012 ER - TY - CHAP A1 - Schmaltz, Christian A1 - Pokutta, Sebastian A1 - Heidorn, Thomas A1 - Andrae, Silvio T1 - How to make regulators and shareholders happy under Basel III T2 - Proceedings of the 26th Australasian Finance and Banking Conference Y1 - 2013 UR - http://ssrn.com/abstract=2179490 N1 - Additional Note: 10.1016/j.jbankfin.2014.05.031 ER - TY - CHAP A1 - Briët, Jop A1 - Dadush, Daniel A1 - Pokutta, Sebastian T1 - On the existence of 0/1 polytopes with high semidefinite extension complexity T2 - Proceedings of ESA Y1 - 2013 ER - TY - CHAP A1 - Braun, Gabor A1 - Pokutta, Sebastian T1 - Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium T2 - Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium Y1 - 2013 UR - http://eccc.hpi-web.de/report/2013/056 SP - 688 EP - 697 ER - TY - CHAP A1 - Braun, Gábor A1 - Fiorini, Samuel A1 - Pokutta, Sebastian T1 - Average case polyhedral complexity of the maximum stable set problem T2 - Proceedings of RANDOM Y1 - 2014 N1 - Additional Note: http://www.dagstuhl.de/dagpub/978-3-939897-74-3 N1 - URL of the PDF: http://www.dagstuhl.de/dagpub/978-3-939897-74-3 ER - TY - CHAP A1 - Braun, Gábor A1 - Pokutta, Sebastian A1 - Xie, Y. T1 - Info-Greedy Sequential Adaptive Compressed Sensing T2 - Proceedings of 52nd Annual Allerton Conference on Communication, Control, and Computing Y1 - 2014 ER - TY - CHAP A1 - Bazzi, Abbas A1 - Fiorini, Samuel A1 - Pokutta, Sebastian A1 - Svensson, Ola T1 - Small linear programs cannot approximate Vertex Cover within a factor of 2 - epsilon T2 - Proceedings of FOCS Y1 - 2015 N1 - URL of the Slides: https://app.box.com/s/00aqupo722bbpmdcyzwt8xmfqzyu14iy ER - TY - CHAP A1 - Pokutta, Sebastian A1 - Schmaltz, Christian T1 - Optimal degree of centralization of liquidity management T2 - Proceedings of 22nd Australasian Finance and Banking Conference Y1 - 2009 UR - http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1458963 ER - TY - CHAP A1 - Braun, Gábor A1 - Pokutta, Sebastian T1 - Rank of random half-integral polytopes T2 - Electronic Notes in Discrete Mathematics Y1 - 2010 UR - http://www.box.net/shared/0zx04174bg N1 - Additional Note: doi:10.1016/j.endm.2010.05.053 N1 - URL of the PDF: http://dx.doi.org/10.1016/j.endm.2010.05.053 VL - 36 SP - 415 EP - 422 ER - TY - CHAP A1 - Drewes, S. A1 - Pokutta, Sebastian T1 - Cutting-planes for weakly-coupled 0/1 second order cone programs T2 - Electronic Notes in Discrete Mathematics Y1 - 2010 UR - http://www.box.net/shared/4k2qtsl65k N1 - Additional Note: doi:10.1016/j.endm.2010.05.093 N1 - URL of the PDF: http://dx.doi.org/j.endm.2010.05.093 VL - 36 SP - 735 EP - 742 ER - TY - CHAP A1 - Drewes, S. A1 - Pokutta, Sebastian T1 - Geometric mean maximization in the presence of discrete decisions T2 - Proceedings of 23rd Australasian Finance and Banking Conference Y1 - 2010 ER - TY - CHAP A1 - Martin, Alexander A1 - Müller, J. A1 - Pokutta, Sebastian T1 - On clearing coupled day-ahead electricity markets T2 - Proceedings of 23rd Australasian Finance and Banking Conference Y1 - 2010 UR - http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1660528 N1 - Additional Note: doi:10.1080/10556788.2013.823544 ER - TY - CHAP A1 - Pokutta, Sebastian A1 - Schulz, A.S. T1 - On the rank of generic cutting-plane proof systems T2 - Proceedings of IPCO, Lecture Notes in Computer Science Y1 - 2010 UR - http://www.optimization-online.org/DB_HTML/2013/02/3782.html N1 - Additional Note: doi:10.1007/978-3-642-13036-6_34 N1 - URL of the PDF: http://dx.doi.org/10.1007/978-3-642-13036-6_34 VL - 6080 SP - 450 EP - 463 ER - TY - CHAP A1 - Dey, Santanu Sabush A1 - Pokutta, Sebastian T1 - Design and verify: a new scheme for generating cutting-planes T2 - Proceedings of IPCO, Lecture Notes in Computer Science Y1 - 2011 UR - http://www.optimization-online.org/DB_HTML/2011/04/3002.html N1 - Additional Note: DOI: 10.1007/978-3-642-20807-2_12 VL - 6655 SP - 143 EP - 155 ER - TY - CHAP A1 - Helmke, H. A1 - Gluchshenko, O. A1 - Martin, Alexander A1 - Peter, A. A1 - Pokutta, Sebastian A1 - Siebert, U. T1 - Optimal Mixed-Mode Runway Scheduling T2 - Proceedings of DACS Y1 - 2011 UR - http://www.box.net/shared/34p1ea2of7exdgq7tlty ER - TY - CHAP A1 - Pokutta, Sebastian A1 - Schmaltz, Christian T1 - A network model for bank lending capacity T2 - Proceedings of Systemic Risk, Basel III, Financial Stability and Regulation Y1 - 2011 UR - http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1773964 ER - TY - CHAP A1 - Braun, Gábor A1 - Pokutta, Sebastian T1 - The matching polytope does not admit fully-polynomial size relaxation schemes T2 - Proceeedings of SODA Y1 - 2015 ER - TY - CHAP A1 - Braun, Gábor A1 - Pokutta, Sebastian A1 - Zink, Daniel T1 - Inapproximability of combinatorial problems via small LPs and SDPs T2 - Proceeedings of STOC Y1 - 2015 ER - TY - CHAP A1 - Pokutta, Sebastian T1 - Information Theory and Polyhedral Combinatorics T2 - Proceedings of 53rd Annual Allerton Conference on Communication, Control, and Computing Y1 - 2015 N1 - Additional Note: http://ieeexplore.ieee.org/abstract/document/7447134/ N1 - URL of the PDF: http://ieeexplore.ieee.org/abstract/document/7447134/ ER - TY - CHAP A1 - Song, R. A1 - Xie, Y. A1 - Pokutta, Sebastian T1 - Sequential Sensing with Model Mismatch T2 - Proceedings of ISIT Y1 - 2015 ER - TY - CHAP A1 - Xie, Y. A1 - Li, Q. A1 - Pokutta, Sebastian T1 - Supervised Online Subspace Tracking T2 - Proceedings of Asilomar Conference on Signals, Systems, and Computers Y1 - 2015 ER - TY - CHAP A1 - Roy, Aurko A1 - Pokutta, Sebastian T1 - Hierarchical Clustering via Spreading Metrics T2 - Proceedings of NIPS Y1 - 2016 N1 - Additional Note: http://https://papers.nips.cc/paper/by-source-2016-1199 https://arxiv.org/abs/1610.09269 N1 - URL of the PDF: https://papers.nips.cc/paper/by-source-2016-1199 ER - TY - CHAP A1 - Braun, Gábor A1 - Roy, Aurko A1 - Pokutta, Sebastian T1 - Stronger Reductions for Extended Formulations T2 - Proceedings of IPCO Y1 - 2016 ER - TY - CHAP A1 - Braun, Gábor A1 - Brown-Cohen, Jonah A1 - Huq, Arefin A1 - Pokutta, Sebastian A1 - Raghavendra, Prasad A1 - Weitz, Benjamin A1 - Zink, Daniel T1 - The matching problem has no small symmetric SDP T2 - Proceddings of SODA 2016 Y1 - 2016 N1 - URL of the PDF: http://ieeexplore.ieee.org/abstract/document/7447134/ ER - TY - CHAP A1 - Roy, Aurko A1 - Xu, Huan A1 - Pokutta, Sebastian T1 - Reinforcement Learning under Model Mismatch T2 - Proceedings of NIPS Y1 - 2017 N1 - Additional Note: http://https://papers.nips.cc/paper/by-source-2016-1199 https://arxiv.org/abs/1610.09269 ER - TY - CHAP A1 - Bärmann, Andreas A1 - Pokutta, Sebastian A1 - Schneider, Oskar T1 - Emulating the Expert: Inverse Optimization through Online Learning T2 - Proceedings of the International Conference on Machine Learning (ICML) Y1 - 2017 N1 - URL of the PDF: http://proceedings.mlr.press/v70/barmann17a.html N1 - URL of the Poster: https://app.box.com/s/h3ychkv99il7oyzw7sym8la5lhit3elg N1 - URL of the Slides: https://app.box.com/s/7ti8sz8mf5s2znn1qo32in4jxy5gjnn1 N1 - URL of the Abstract: http://www.pokutta.com/blog/research/2018/11/25/expertLearning-abstract.html ER - TY - CHAP A1 - Sofranac, Boro A1 - Gleixner, Ambros A1 - Pokutta, Sebastian T1 - An Algorithm-Independent Measure of Progress for Linear Constraint Propagation T2 - 27th International Conference on Principles and Practice of Constraint Programming (CP 2021) N2 - Propagation of linear constraints has become a crucial sub-routine in modern Mixed-Integer Programming (MIP) solvers. In practice, iterative algorithms with tolerance-based stopping criteria are used to avoid problems with slow or infinite convergence. However, these heuristic stopping criteria can pose difficulties for fairly comparing the efficiency of different implementations of iterative propagation algorithms in a real-world setting. Most significantly, the presence of unbounded variable domains in the problem formulation makes it difficult to quantify the relative size of reductions performed on them. In this work, we develop a method to measure -- independently of the algorithmic design -- the progress that a given iterative propagation procedure has made at a given point in time during its execution. Our measure makes it possible to study and better compare the behavior of bounds propagation algorithms for linear constraints. We apply the new measure to answer two questions of practical relevance: (i) We investigate to what extent heuristic stopping criteria can lead to premature termination on real-world MIP instances. (ii) We compare a GPU-parallel propagation algorithm against a sequential state-of-the-art implementation and show that the parallel version is even more competitive in a real-world setting than originally reported. Y1 - 2021 U6 - https://doi.org/10.4230/LIPIcs.CP.2021.52 VL - 210 SP - 52:1 EP - 52:17 ER - TY - JOUR A1 - Carderera, Alejandro A1 - Pokutta, Sebastian T1 - Second-order Conditional Gradient Sliding N2 - Constrained second-order convex optimization algorithms are the method of choice when a high accuracy solution to a problem is needed, due to their local quadratic convergence. These algorithms require the solution of a constrained quadratic subproblem at every iteration. We present the \emph{Second-Order Conditional Gradient Sliding} (SOCGS) algorithm, which uses a projection-free algorithm to solve the constrained quadratic subproblems inexactly. When the feasible region is a polytope the algorithm converges quadratically in primal gap after a finite number of linearly convergent iterations. Once in the quadratic regime the SOCGS algorithm requires O(log(log1/ε)) first-order and Hessian oracle calls and O(log(1/ε)log(log1/ε)) linear minimization oracle calls to achieve an ε-optimal solution. This algorithm is useful when the feasible region can only be accessed efficiently through a linear optimization oracle, and computing first-order information of the function, although possible, is costly. Y1 - 2020 ER - TY - JOUR A1 - Mathieu, Besançon A1 - Carderera, Alejandro A1 - Pokutta, Sebastian T1 - FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and Conditional Gradients JF - INFORMS Journal on Computing N2 - We present FrankWolfe.jl, an open-source implementation of several popular Frank–Wolfe and conditional gradients variants for first-order constrained optimization. The package is designed with flexibility and high performance in mind, allowing for easy extension and relying on few assumptions regarding the user-provided functions. It supports Julia’s unique multiple dispatch feature, and it interfaces smoothly with generic linear optimization formulations using MathOptInterface.jl. Y1 - 2022 U6 - https://doi.org/10.1287/ijoc.2022.1191 VL - 34 IS - 5 SP - 2383 EP - 2865 ER - TY - JOUR A1 - Abbas, Amira A1 - Ambainis, Andris A1 - Augustino, Brandon A1 - Bärtschi, Andreas A1 - Buhrman, Harry A1 - Coffrin, Carleton A1 - Cortiana, Giorgio A1 - Dunjko, Vedran A1 - Egger, Daniel J. A1 - Elmegreen, Bruce G. A1 - Franco, Nicola A1 - Fratini, Filippo A1 - Fuller, Bryce A1 - Gacon, Julien A1 - Gonciulea, Constantin A1 - Gribling, Sander A1 - Gupta, Swati A1 - Hadfield, Stuart A1 - Heese, Raoul A1 - Kircher, Gerhard A1 - Kleinert, Thomas A1 - Koch, Thorsten A1 - Korpas, Georgios A1 - Lenk, Steve A1 - Marecek, Jakub A1 - Markov, Vanio A1 - Mazzola, Guglielmo A1 - Mensa, Stefano A1 - Mohseni, Naeimeh A1 - Nannicini, Giacomo A1 - O’Meara, Corey A1 - Tapia, Elena Peña A1 - Pokutta, Sebastian A1 - Proissl, Manuel A1 - Rebentrost, Patrick A1 - Sahin, Emre A1 - Symons, Benjamin C. B. A1 - Tornow, Sabine A1 - Valls, Víctor A1 - Woerner, Stefan A1 - Wolf-Bauwens, Mira L. A1 - Yard, Jon A1 - Yarkoni, Sheir A1 - Zechiel, Dirk A1 - Zhuk, Sergiy A1 - Zoufal, Christa T1 - Challenges and opportunities in quantum optimization JF - Nature Reviews Physics Y1 - 2024 U6 - https://doi.org/10.1038/s42254-024-00770-9 SN - 2522-5820 VL - 6 SP - 718 EP - 735 PB - Springer Science and Business Media LLC ER - TY - CHAP A1 - Sofranac, Boro A1 - Gleixner, Ambros A1 - Pokutta, Sebastian T1 - Accelerating Domain Propagation: an Efficient GPU-Parallel Algorithm over Sparse Matrices T2 - 2020 IEEE/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (IA3) N2 - Fast domain propagation of linear constraints has become a crucial component of today's best algorithms and solvers for mixed integer programming and pseudo-boolean optimization to achieve peak solving performance. Irregularities in the form of dynamic algorithmic behaviour, dependency structures, and sparsity patterns in the input data make efficient implementations of domain propagation on GPUs and, more generally, on parallel architectures challenging. This is one of the main reasons why domain propagation in state-of-the-art solvers is single thread only. In this paper, we present a new algorithm for domain propagation which (a) avoids these problems and allows for an efficient implementation on GPUs, and is (b) capable of running propagation rounds entirely on the GPU, without any need for synchronization or communication with the CPU. We present extensive computational results which demonstrate the effectiveness of our approach and show that ample speedups are possible on practically relevant problems: on state-of-the-art GPUs, our geometric mean speed-up for reasonably-large instances is around 10x to 20x and can be as high as 195x on favorably-large instances. Y1 - 2020 U6 - https://doi.org/10.1109/IA351965.2020.00007 N1 - URL of the Slides: https://app.box.com/s/qy0pjmhtbm7shk2ypxjxlh2sj4nudvyu N1 - URL of the Abstract: http://www.pokutta.com/blog/research/2020/09/20/gpu-prob.html SP - 1 EP - 11 ER - TY - JOUR A1 - Šofranac, Boro A1 - Gleixner, Ambros A1 - Pokutta, Sebastian T1 - Accelerating domain propagation: An efficient GPU-parallel algorithm over sparse matrices JF - Parallel Computing N2 - • Currently, domain propagation in state-of-the-art MIP solvers is single thread only. • The paper presents a novel, efficient GPU algorithm to perform domain propagation. • Challenges are dynamic algorithmic behavior, dependency structures, sparsity patterns. • The algorithm is capable of running entirely on the GPU with no CPU involvement. • We achieve speed-ups of around 10x to 20x, up to 180x on favorably-large instances. Y1 - 2022 U6 - https://doi.org/10.1016/j.parco.2021.102874 VL - 109 SP - 102874 ER - TY - CHAP A1 - Diakonikolas, Jelena A1 - Carderera, Alejandro A1 - Pokutta, Sebastian T1 - Breaking the Curse of Dimensionality (Locally) to Accelerate Conditional Gradients T2 - OPTML Workshop Paper Y1 - 2019 N1 - URL of the Code: https://colab.research.google.com/drive/1ejjfCan7xnEhWWJXCIzb03CwQRG9iW_O N1 - URL of the PDF: https://opt-ml.org/papers/2019/paper_26.pdf N1 - URL of the Poster: https://app.box.com/s/d7p038u7df422q4jsccbmj15mngqv2ws N1 - URL of the Slides: https://app.box.com/s/gphkhapso7d1vrfnzqykkb3vx0agxh8w N1 - URL of the Abstract: http://www.pokutta.com/blog/research/2019/07/04/LaCG-abstract.html ER - TY - CHAP A1 - Combettes, Cyrille W. A1 - Pokutta, Sebastian T1 - Blended Matching Pursuit T2 - Proceedings of NeurIPS Y1 - 2019 N1 - URL of the Code: https://colab.research.google.com/drive/17XYIxnCcJjKswba9mAaXFWnNGVZdsaXQ N1 - URL of the PDF: https://papers.nips.cc/paper/8478-blended-matching-pursuit N1 - URL of the Poster: https://app.box.com/s/j6lbh49udjd45krhxcypufxwdofw6br8 N1 - URL of the Slides: https://app.box.com/s/8lfktq6h3dqp9t2gqydu2tp8h2uxgz7m N1 - URL of the Abstract: http://www.pokutta.com/blog/research/2019/05/27/bmp-abstract.html ER - TY - CHAP A1 - Pokutta, Sebastian A1 - Singh, M. A1 - Torrico, A. T1 - On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness T2 - OPTML Workshop Paper Y1 - 2019 N1 - URL of the PDF: https://opt-ml.org/papers/2019/paper_16.pdf N1 - URL of the Poster: https://app.box.com/s/24vbh1s2vib11upqyepzen3lzdl13skr ER - TY - CHAP A1 - Criado, Francisco A1 - Martínez-Rubio, David A1 - Pokutta, Sebastian T1 - Fast Algorithms for Packing Proportional Fairness and its Dual T2 - Proceedings of the Conference on Neural Information Processing Systems N2 - The proportional fair resource allocation problem is a major problem studied in flow control of networks, operations research, and economic theory, where it has found numerous applications. This problem, defined as the constrained maximization of sum_i log x_i, is known as the packing proportional fairness problem when the feasible set is defined by positive linear constraints and x ∈ R≥0. In this work, we present a distributed accelerated first-order method for this problem which improves upon previous approaches. We also design an algorithm for the optimization of its dual problem. Both algorithms are width-independent. Y1 - 2022 VL - 36 ER - TY - CHAP A1 - Carderera, Alejandro A1 - Pokutta, Sebastian A1 - Mathieu, Besançon T1 - Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions T2 - Thirty-fifth Conference on Neural Information Processing Systems, NeurIPS 2021 N2 - Generalized self-concordance is a key property present in the objective function of many important learning problems. We establish the convergence rate of a simple Frank-Wolfe variant that uses the open-loop step size strategy 𝛾𝑡 = 2/(𝑡 + 2), obtaining a O (1/𝑡) convergence rate for this class of functions in terms of primal gap and Frank-Wolfe gap, where 𝑡 is the iteration count. This avoids the use of second-order information or the need to estimate local smoothness parameters of previous work. We also show improved convergence rates for various common cases, e.g., when the feasible region under consideration is uniformly convex or polyhedral. Y1 - 2021 ER - TY - JOUR A1 - Kerdreux, Thomas A1 - d'Aspremont, Alexandre A1 - Pokutta, Sebastian T1 - Local and Global Uniform Convexity Conditions N2 - We review various characterizations of uniform convexity and smoothness on norm balls in finite-dimensional spaces and connect results stemming from the geometry of Banach spaces with scaling inequalities used in analysing the convergence of optimization methods. In particular, we establish local versions of these conditions to provide sharper insights on a recent body of complexity results in learning theory, online learning, or offline optimization, which rely on the strong convexity of the feasible set. While they have a significant impact on complexity, these strong convexity or uniform convexity properties of feasible sets are not exploited as thoroughly as their functional counterparts, and this work is an effort to correct this imbalance. We conclude with some practical examples in optimization and machine learning where leveraging these conditions and localized assumptions lead to new complexity results. Y1 - 2021 ER - TY - CHAP A1 - Zimmer, Max A1 - Spiegel, Christoph A1 - Pokutta, Sebastian T1 - How I Learned to Stop Worrying and Love Retraining T2 - Proceedings of International Conference on Learning Representations Y1 - 2023 ER - TY - CHAP A1 - Wirth, Elias A1 - Kerdreux, Thomas A1 - Pokutta, Sebastian T1 - Acceleration of Frank-Wolfe Algorithms with Open Loop Step-sizes T2 - Proceedings of International Conference on Artificial Intelligence and Statistics Y1 - 2023 ER - TY - CHAP A1 - Wirth, Elias A1 - Kera, A1 - Pokutta, Sebastian T1 - Approximate Vanishing Ideal Computations at Scale T2 - Proceedings of International Conference on Learning Representations Y1 - 2023 ER - TY - JOUR A1 - Bienstock, Daniel A1 - Muñoz, Gonzalo A1 - Pokutta, Sebastian T1 - Principled Deep Neural Network Training Through Linear Programming JF - Discrete Optimization N2 - Deep learning has received much attention lately due to the impressive empirical performance achieved by training algorithms. Consequently, a need for a better theoretical understanding of these problems has become more evident and multiple works in recent years have focused on this task. In this work, using a unified framework, we show that there exists a polyhedron that simultaneously encodes, in its facial structure, all possible deep neural network training problems that can arise from a given architecture, activation functions, loss function, and sample size. Notably, the size of the polyhedral representation depends only linearly on the sample size, and a better dependency on several other network parameters is unlikely. Using this general result, we compute the size of the polyhedral encoding for commonly used neural network architectures. Our results provide a new perspective on training problems through the lens of polyhedral theory and reveal strong structure arising from these problems. Y1 - 2023 U6 - https://doi.org/10.1016/j.disopt.2023.100795 VL - 49 ER - TY - CHAP A1 - Głuch, Grzegorz A1 - Turan, Berkant A1 - Nagarajan, Sai Ganesh A1 - Pokutta, Sebastian T1 - The Good, the Bad and the Ugly: Meta-Analysis of Watermarks, Transferable Attacks and Adversarial Defenses T2 - Proceedings of the Conference on Neural Information Processing Systems Y1 - 2025 ER - TY - CHAP A1 - Kera, Hiroshi A1 - Pelleriti, Nico A1 - Ishihara, Yuki A1 - Zimmer, Max A1 - Pokutta, Sebastian T1 - Computational Algebra with Attention: Transformer Oracles for Border Basis Algorithms T2 - Proceedings of the Conference on Neural Information Processing Systems Y1 - 2025 ER - TY - JOUR A1 - Hendrych, Deborah A1 - Troppens, Hannah A1 - Besançon, Mathieu A1 - Pokutta, Sebastian T1 - Convex mixed-integer optimization with Frank-Wolfe methods JF - Mathematical Programming Computation Y1 - 2025 U6 - https://doi.org/10.1007/s12532-025-00288-w VL - 17 SP - 731 EP - 757 ER - TY - CHAP A1 - Hendrych, Deborah A1 - Besançon, Mathieu A1 - Martínez-Rubio, David A1 - Pokutta, Sebastian T1 - Secant line search for Frank-Wolfe algorithms T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/hendrych25a/hendrych25a.pdf VL - 267 SP - 23005 EP - 23029 ER - TY - CHAP A1 - Pauls, Jan A1 - Zimmer, Max A1 - Turan, Berkant A1 - Saatchi, Sassan A1 - Ciais, Philippe A1 - Pokutta, Sebastian A1 - Gieseke, Fabian T1 - Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/pauls25a/pauls25a.pdf VL - 267 SP - 48422 EP - 48438 ER - TY - CHAP A1 - Mundinger, Konrad A1 - Zimmer, Max A1 - Kiem, Aldo A1 - Spiegel, Christoph A1 - Pokutta, Sebastian T1 - Neural Discovery in Mathematics: Do Machines Dream of Colored Planes? T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/mundinger25a/mundinger25a.pdf VL - 267 SP - 45236 EP - 45255 ER - TY - CHAP A1 - Pelleriti, Nico A1 - Zimmer, Max A1 - Wirth, Elias A1 - Pokutta, Sebastian T1 - Approximating Latent Manifolds in Neural Networks via Vanishing Ideals T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/pelleriti25a/pelleriti25a.pdf VL - 267 SP - 48734 EP - 48761 ER - TY - CHAP A1 - Roux, Christophe A1 - Martínez-Rubio, David A1 - Pokutta, Sebastian T1 - Implicit Riemannian optimism with applications to min-max problems T2 - Proceedings of the 42nd International Conference on Machine Learning Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v267/main/assets/roux25a/roux25a.pdf VL - 267 SP - 52139 EP - 52172 ER - TY - CHAP A1 - Turan, Berkant A1 - Asadulla, Suhrab A1 - Steinmann, David A1 - Stammer, Wolfgang A1 - Pokutta, Sebastian T1 - Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings T2 - Proceedings of the ICML Workshop on Actionable Interpretability Y1 - 2025 ER - TY - JOUR A1 - Carderera, Alejandro A1 - Besançon, Mathieu A1 - Pokutta, Sebastian T1 - Scalable Frank-Wolfe on generalized self-concordant functions via simple steps JF - SIAM Journal on Optimization Y1 - 2024 U6 - https://doi.org/10.1137/23M1616789 VL - 34 IS - 3 ER - TY - JOUR A1 - Parczyk, Olaf A1 - Pokutta, Sebastian A1 - Spiegel, Christoph A1 - Szabó, Tibor T1 - New Ramsey multiplicity bounds and search heuristics JF - Foundations of Computational Mathematics Y1 - 2024 U6 - https://doi.org/10.1007/s10208-024-09675-6 ER - TY - CHAP A1 - Pauls, Jan A1 - Zimmer, Max A1 - Kelly, Una M A1 - Schwartz, Martin A1 - Saatchi, Sassan A1 - Ciais, Philippe A1 - Pokutta, Sebastian A1 - Brandt, Martin A1 - Gieseke, Fabian T1 - Estimating canopy height at scale T2 - Proceedings of the 41st International Conference on Machine Learning N2 - We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the reliability of our predictions in those areas. A comparison between predictions and ground-truth labels yields an MAE/RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters, which depicts a substantial improvement compared to existing global-scale products. The resulting height map as well as the underlying framework will facilitate and enhance ecological analyses at a global scale, including, but not limited to, large-scale forest and biomass monitoring. Y1 - 2024 UR - https://raw.githubusercontent.com/mlresearch/v235/main/assets/pauls24a/pauls24a.pdf VL - 235 SP - 39972 EP - 39988 ER - TY - CHAP A1 - Hendrych, Deborah A1 - Besançon, Mathieu A1 - Pokutta, Sebastian T1 - Solving the optimal experiment design problem with mixed-integer convex methods T2 - 22nd International Symposium on Experimental Algorithms (SEA 2024) N2 - We tackle the Optimal Experiment Design Problem, which consists of choosing experiments to run or observations to select from a finite set to estimate the parameters of a system. The objective is to maximize some measure of information gained about the system from the observations, leading to a convex integer optimization problem. We leverage Boscia.jl, a recent algorithmic framework, which is based on a nonlinear branch-and-bound algorithm with node relaxations solved to approximate optimality using Frank-Wolfe algorithms. One particular advantage of the method is its efficient utilization of the polytope formed by the original constraints which is preserved by the method, unlike alternative methods relying on epigraph-based formulations. We assess our method against both generic and specialized convex mixed-integer approaches. Computational results highlight the performance of our proposed method, especially on large and challenging instances. Y1 - 2024 U6 - https://doi.org/10.4230/LIPIcs.SEA.2024.16 VL - 301 SP - 16:1 EP - 16:22 ER - TY - CHAP A1 - Kiem, Aldo A1 - Pokutta, Sebastian A1 - Spiegel, Christoph T1 - Categorification of Flag Algebras T2 - Discrete Mathematics Days 2024 Y1 - 2024 U6 - https://doi.org/10.37536/TYSP5643 SP - 259 EP - 264 ER - TY - CHAP A1 - Kiem, Aldo A1 - Pokutta, Sebastian A1 - Spiegel, Christoph T1 - The Four-Color Ramsey Multiplicity of Triangles T2 - Discrete Mathematics Days 2024 Y1 - 2024 U6 - https://doi.org/10.37536/TYSP5643 SP - 13 EP - 18 ER - TY - JOUR A1 - Mundinger, Konrad A1 - Pokutta, Sebastian A1 - Spiegel, Christoph A1 - Zimmer, Max T1 - Extending the Continuum of Six-Colorings JF - Geombinatorics Quarterly Y1 - 2024 VL - 34 IS - 1 SP - 20 EP - 29 ER - TY - CHAP A1 - Mundinger, Konrad A1 - Pokutta, Sebastian A1 - Spiegel, Christoph A1 - Zimmer, Max T1 - Extending the Continuum of Six-Colorings T2 - Discrete Mathematics Days 2024 Y1 - 2024 U6 - https://doi.org/10.37536/TYSP5643 SP - 178 EP - 183 ER - TY - CHAP A1 - Martínez-Rubio, David A1 - Roux, Christophe A1 - Pokutta, Sebastian T1 - Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point T2 - Proceedings of the 41st International Conference on Machine Learning N2 - In this work, we analyze two of the most fundamental algorithms in geodesically convex optimization: Riemannian gradient descent and (possibly inexact) Riemannian proximal point. We quantify their rates of convergence and produce different variants with several trade-offs. Crucially, we show the iterates naturally stay in a ball around an optimizer, of radius depending on the initial distance and, in some cases, on the curvature. Previous works simply assumed bounded iterates, resulting in rates that were not fully quantified. We also provide an implementable inexact proximal point algorithm and prove several new useful properties of Riemannian proximal methods: they work when positive curvature is present, the proximal operator does not move points away from any optimizer, and we quantify the smoothness of its induced Moreau envelope. Further, we explore beyond our theory with empirical tests. Y1 - 2024 UR - https://raw.githubusercontent.com/mlresearch/v235/main/assets/marti-nez-rubio24a/marti-nez-rubio24a.pdf VL - 235 SP - 34920 EP - 34948 ER - TY - JOUR A1 - Deza, Antoine A1 - Pokutta, Sebastian A1 - Pournin, Lionel T1 - The complexity of geometric scaling JF - Operations Research Letters Y1 - 2024 U6 - https://doi.org/10.1016/j.orl.2023.11.010 VL - 52 SP - 107057 ER - TY - JOUR A1 - Pokutta, Sebastian T1 - The Frank-Wolfe algorithm: a short introduction JF - Jahresbericht der Deutschen Mathematiker-Vereinigung Y1 - 2024 U6 - https://doi.org/10.1365/s13291-023-00275-x VL - 126 SP - 3 EP - 35 ER - TY - CHAP A1 - Wäldchen, Stephan A1 - Sharma, Kartikey A1 - Turan, Berkant A1 - Zimmer, Max A1 - Pokutta, Sebastian T1 - Interpretability Guarantees with Merlin-Arthur Classifiers T2 - Proceedings of The 27th International Conference on Artificial Intelligence and Statistics N2 - We propose an interactive multi-agent classifier that provides provable interpretability guarantees even for complex agents such as neural networks. These guarantees consist of lower bounds on the mutual information between selected features and the classification decision. Our results are inspired by the Merlin-Arthur protocol from Interactive Proof Systems and express these bounds in terms of measurable metrics such as soundness and completeness. Compared to existing interactive setups, we rely neither on optimal agents nor on the assumption that features are distributed independently. Instead, we use the relative strength of the agents as well as the new concept of Asymmetric Feature Correlation which captures the precise kind of correlations that make interpretability guarantees difficult. We evaluate our results on two small-scale datasets where high mutual information can be verified explicitly. Y1 - 2024 UR - https://proceedings.mlr.press/v238/waldchen24a/waldchen24a.pdf VL - 238 SP - 1963 EP - 1971 ER - TY - CHAP A1 - Zimmer, Max A1 - Spiegel, Christoph A1 - Pokutta, Sebastian T1 - Sparse Model Soups T2 - 12th International Conference on Learning Representations (ICLR 2024) Y1 - 2024 SN - 9781713898658 PB - Curran Associates, Inc. ER - TY - JOUR A1 - Stengl, Steven-Marian A1 - Gelß, Patrick A1 - Klus, Stefan A1 - Pokutta, Sebastian T1 - Existence and uniqueness of solutions of the Koopman--von Neumann equation on bounded domains JF - Journal of Physics A: Mathematical and Theoretical Y1 - 2024 U6 - https://doi.org/10.1088/1751-8121/ad6f7d VL - 57 IS - 39 ER - TY - JOUR A1 - Deza, Antoine A1 - Onn, Shmuel A1 - Pokutta, Sebastian A1 - Pournin, Lionel T1 - Kissing polytopes JF - SIAM Journal on Discrete Mathematics N2 - We investigate the following question: How close can two disjoint lattice polytopes contained in a fixed hypercube be? This question stems from various contexts where the minimal distance between such polytopes appears in complexity bounds of optimization algorithms. We provide nearly matching bounds on this distance and discuss its exact computation. We also give similar bounds for disjoint rational polytopes whose binary encoding length is prescribed. Y1 - 2024 U6 - https://doi.org/10.1137/24M1640859 VL - 38 IS - 4 ER - TY - JOUR A1 - Kerdreux, Thomas A1 - d'Aspremont, Alexandre A1 - Pokutta, Sebastian T1 - Restarting Frank-Wolfe: Faster Rates under Hölderian Error Bounds JF - Journal of Optimization Theory and Applications Y1 - 2022 ER - TY - JOUR A1 - Combettes, Cyrille A1 - Pokutta, Sebastian T1 - Revisiting the Approximate Carathéodory Problem via the Frank-Wolfe Algorithm JF - Mathematical Programming A Y1 - 2023 U6 - https://doi.org/10.1007/s10107-021-01735-x VL - 197 SP - 191 EP - 214 ER - TY - JOUR A1 - Vu‐Han, Tu‐Lan A1 - Sunkara, Vikram A1 - Bermudez‐Schettino, Rodrigo A1 - Schwechten, Jakob A1 - Runge, Robin A1 - Perka, Carsten A1 - Winkler, Tobias A1 - Pokutta, Sebastian A1 - Weiß, Claudia A1 - Pumberger, Matthias T1 - Feature Engineering for the Prediction of Scoliosis in 5q‐Spinal Muscular Atrophy JF - Journal of Cachexia, Sarcopenia and Muscle Y1 - 2025 U6 - https://doi.org/10.1002/jcsm.13599 VL - 16 IS - 1 SP - e13599 ER - TY - CHAP A1 - Troppens, Hannah A1 - Besançon, Mathieu A1 - Wilken, St. Elmo A1 - Pokutta, Sebastian T1 - Mixed-Integer Optimization for Loopless Flux Distributions in Metabolic Networks T2 - 23rd International Symposium on Experimental Algorithms (SEA 2025) Y1 - 2025 U6 - https://doi.org/10.4230/LIPIcs.SEA.2025.26 VL - 338 SP - 26:1 EP - 26:18 PB - Schloss Dagstuhl – Leibniz-Zentrum für Informatik ER - TY - CHAP A1 - Głuch, Grzegorz A1 - Turan, Berkant A1 - Nagarajan, Sai Ganesh A1 - Pokutta, Sebastian T1 - The good, the bad and the ugly: watermarks, transferable attacks and adversarial defenses T2 - 1st Workshop on GenAI Watermarking, collocated with ICLR 2025 Y1 - 2025 UR - https://openreview.net/pdf?id=rUs5ryYqZe ER - TY - CHAP A1 - Besançon, Mathieu A1 - Pokutta, Sebastian A1 - Wirth, Elias T1 - The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control T2 - Proceedings of The 28th International Conference on Artificial Intelligence and Statistics Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v258/main/assets/besancon25a/besancon25a.pdf VL - 258 SP - 271 EP - 279 ER - TY - CHAP A1 - Martínez-Rubio, David A1 - Roux, Christophe A1 - Criscitiello, Christopher A1 - Pokutta, Sebastian T1 - Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties T2 - Proceedings of The 28th International Conference on Artificial Intelligence and Statistics Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v258/main/assets/martinez-rubio25a/martinez-rubio25a.pdf VL - 258 SP - 280 EP - 288 ER - TY - CHAP A1 - Sadiku, Shpresim A1 - Wagner, Moritz A1 - Pokutta, Sebastian T1 - GSE: Group-wise sparse and explainable adversarial attacks T2 - 13th International Conference on Learning Representations (ICLR 2025) Y1 - 2025 UR - https://openreview.net/pdf?id=d54fIsAbff ER - TY - JOUR A1 - Wirth, Elias A1 - Pena, Javier A1 - Pokutta, Sebastian T1 - Correction: Accelerated affine-invariant convergence rates of the Frank-Wolfe algorithm with open-loop step-sizes JF - Mathematical Programming Y1 - 2025 U6 - https://doi.org/10.1007/s10107-025-02214-3 VL - 214 SP - 941 EP - 942 ER - TY - CHAP A1 - Roux, Christophe A1 - Zimmer, Max A1 - Pokutta, Sebastian T1 - On the byzantine-resilience of distillation-based federated learning T2 - 13th International Conference on Learning Representations (ICLR 2025) N2 - Federated Learning (FL) algorithms using Knowledge Distillation (KD) have received increasing attention due to their favorable properties with respect to privacy, non-i.i.d. data and communication cost. These methods depart from transmitting model parameters and instead communicate information about a learning task by sharing predictions on a public dataset. In this work, we study the performance of such approaches in the byzantine setting, where a subset of the clients act in an adversarial manner aiming to disrupt the learning process. We show that KD-based FL algorithms are remarkably resilient and analyze how byzantine clients can influence the learning process. Based on these insights, we introduce two new byzantine attacks and demonstrate their ability to break existing byzantine-resilient methods. Additionally, we propose a novel defence method which enhances the byzantine resilience of KD-based FL algorithms. Finally, we provide a general framework to obfuscate attacks, making them significantly harder to detect, thereby improving their effectiveness. Y1 - 2025 UR - https://openreview.net/pdf?id=of6EuHT7de ER - TY - CHAP A1 - Sadiku, Shpresim A1 - Wagner, Moritz A1 - Nagarajan, Sai Ganesh A1 - Pokutta, Sebastian T1 - S-CFE: Simple Counterfactual Explanations T2 - Proceedings of The 28th International Conference on Artificial Intelligence and Statistics Y1 - 2025 UR - https://raw.githubusercontent.com/mlresearch/v258/main/assets/sadiku25a/sadiku25a.pdf VL - 258 SP - 172 EP - 180 ER - TY - CHAP A1 - Martínez-Rubio, David A1 - Wirth, Elias A1 - Pokutta, Sebastian T1 - Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond T2 - Proceedings of Machine Learning Research N2 - It has recently been shown that ISTA, an unaccelerated optimization method, presents sparse updates for the ℓ1-regularized undirected personalized PageRank problem (Fountoulakis et al., 2019), leading to cheap iteration complexity and providing the same guarantees as the approximate personalized PageRank algorithm (APPR) (Andersen et al., 2006). In this work, we design an accelerated optimization algorithm for this problem that also performs sparse updates, providing an affirmative answer to the COLT 2022 open question of Fountoulakis and Yang (2022). Acceleration provides a reduced dependence on the condition number, while the dependence on the sparsity in our updates differs from the ISTA approach. Further, we design another algorithm by using conjugate directions to achieve an exact solution while exploiting sparsity. Both algorithms lead to faster convergence for certain parameter regimes. Our findings apply beyond PageRank and work for any quadratic objective whose Hessian is a positive-definite 푀-matrix. Y1 - 2023 UR - https://proceedings.mlr.press/v195/martinez-rubio23a/martinez-rubio23a.pdf VL - 195 SP - 1 EP - 35 ER - TY - JOUR A1 - Combettes, Cyrille A1 - Pokutta, Sebastian T1 - Revisiting the Approximate Carathéodory Problem via the Frank-Wolfe Algorithm JF - Mathematical Programming Y1 - 2023 U6 - https://doi.org/10.1007/s10107-021-01735-x VL - 197 SP - 191 EP - 214 ER - TY - CHAP A1 - Zimmer, Max A1 - Spiegel, Christoph A1 - Pokutta, Sebastian ED - Fackeldey, K. T1 - Compression-aware training of neural networks using Frank-Wolfe T2 - Mathematical Optimization for Machine Learning: Proceedings of the MATH+ Thematic Einstein Semester 2023 Y1 - 2025 U6 - https://doi.org/10.1515/9783111376776-010 SP - 137 EP - 168 PB - De Gruyter ER - TY - JOUR A1 - Bärmann, Andreas A1 - Martin, Alexander A1 - Pokutta, Sebastian A1 - Schneider, Oskar T1 - An Online-Learning Approach to Inverse Optimization Y1 - 2018 ER - TY - CHAP A1 - Carderera, Alejandro A1 - Diakonikolas, Jelena A1 - Lin, Cheuk Yin A1 - Pokutta, Sebastian T1 - Parameter-free Locally Accelerated Conditional Gradients T2 - ICML 2021 N2 - Projection-free conditional gradient (CG) methods are the algorithms of choice for constrained optimization setups in which projections are often computationally prohibitive but linear optimization over the constraint set remains computationally feasible. Unlike in projection-based methods, globally accelerated convergence rates are in general unattainable for CG. However, a very recent work on Locally accelerated CG (LaCG) has demonstrated that local acceleration for CG is possible for many settings of interest. The main downside of LaCG is that it requires knowledge of the smoothness and strong convexity parameters of the objective function. We remove this limitation by introducing a novel, Parameter-Free Locally accelerated CG (PF-LaCG) algorithm, for which we provide rigorous convergence guarantees. Our theoretical results are complemented by numerical experiments, which demonstrate local acceleration and showcase the practical improvements of PF-LaCG over non-accelerated algorithms, both in terms of iteration count and wall-clock time. Y1 - 2021 ER - TY - JOUR A1 - Bienenstock, Daniel A1 - Muñoz, Gonzalo A1 - Pokutta, Sebastian T1 - Principled Deep Neural Network Training through Linear Programming N2 - Deep Learning has received significant attention due to its impressive performance in many state-of-the-art learning tasks. Unfortunately, while very powerful, Deep Learning is not well understood theoretically and in particular only recently results for the complexity of training deep neural networks have been obtained. In this work we show that large classes of deep neural networks with various architectures (e.g., DNNs, CNNs, Binary Neural Networks, and ResNets), activation functions (e.g., ReLUs and leaky ReLUs), and loss functions (e.g., Hinge loss, Euclidean loss, etc) can be trained to near optimality with desired target accuracy using linear programming in time that is exponential in the input data and parameter space dimension and polynomial in the size of the data set; improvements of the dependence in the input dimension are known to be unlikely assuming P≠NP, and improving the dependence on the parameter space dimension remains open. In particular, we obtain polynomial time algorithms for training for a given fixed network architecture. Our work applies more broadly to empirical risk minimization problems which allows us to generalize various previous results and obtain new complexity results for previously unstudied architectures in the proper learning setting. Y1 - 2018 ER -