Publications

Preprints and submitted manuscripts

[P3] Han Zhang and Axel Ringh. Statistically consistent inverse optimal control for discrete-time indefinite linear-quadratic systems. Accepted, 2024. Preprint: arXiv:2212.08426.

[P2] Axel Ringh, Xin Mao, Wei Chen, Li Qiu, and Sei Zhen Khong . Gain and phase type multipliers for structured feedback robustness. Accepted, 2024. Preprint: arXiv:2203.11837.

[P1] Axel Ringh, Isabel Haasler, Yongxin Chen, and Johan Karlsson. Graph-structured tensor optimization for nonlinear density control and mean field games. Accepted, 2024. Preprint: arXiv:2112.05645.

Journal papers

[J15] Han Zhang and Axel Ringh. Inverse optimal control for averaged cost per stage linear quadratic regulators. Systems & Control Letters, 183:105658, 2023. https://doi.org/10.1016/j.sysconle.2023.105658. Preprint: arXiv:2305.15332.

[J14] Axel Ringh, Isabel Haasler, Yongxin Chen, and Johan Karlsson. Mean field type control with species dependent dynamics via structured tensor optimization. IEEE Control Systems Letters, 7:2475-1456, 2023. https://doi.org/10.1109/LCSYS.2023.3289050. Preprint: arXiv:2305.15292.

[J13] Isabel Haasler, Axel Ringh, Yongxin Chen, and Johan Karlsson. Scalable computation of dynamic flow problems via multi-marginal graph-structured optimal transport. Accepted to Mathematics of Operations Research, 2023. Preprint: arXiv:2106.14485.

[J12] Han Zhang and Axel Ringh. Inverse Linear-Quadratic Discrete-Time Finite-Horizon Optimal Control for Indistinguishable Agents: A Convex Optimization Approach. Automatica, 148:110758 , 2023. https://doi.org/10.1016/j.automatica.2022.110758. Preprint:  arXiv:2109.01040.

[J11] Di Zhao, Axel Ringh, Li Qiu, and Sei Zhen Khong. Low Phase-Rank Approximation. Linear Algebra and its Applications, 639(15):177-204, 2022. https://doi.org/10.1016/j.laa.2022.01.003. Preprint: arXiv:2011.14811.

[J10] Axel Ringh and Li Qiu. Finsler geometries on strictly accretive matrices. Linear and Multilinear Algebra, 70(21):6753-6771, 2022 (online 2021). https://doi.org/10.1080/03081087.2021.1968781. Preprint: arXiv:2011.13575.

[J9] Isabel Haasler, Johan Karlsson, and Axel Ringh. Control and estimation of ensembles via structured optimal transport: A computational approach based on entropy-regularized multi-marginal optimal transport. IEEE Control Systems Magazine, 41(4): 50-69, 2021. https://doi.org/10.1109/MCS.2021.3076540.

[J8] Isabel Haasler, Axel Ringh, Yongxin Chen, and Johan Karlsson. Multimarginal Optimal Transport with a Tree-Structured Cost and the Schrödinger Bridge Problem. SIAM Journal on Control and Optimization, 59(4):2428-2453, 2021. Received the SIAM Activity Group on Control and Systems Theory Best SICON Paper Prize 2023. https://doi.org/10.1137/20M1320195. Preprint: arXiv:2004.06909.

[J7] Axel Ringh, Johan Karlsson, and Anders Lindquist. An analytic interpolation approach to stability margins with emphasis on time delay. IEEE Transactions on Automatic Control, 67(1):105-120, 2022. https://doi.org/10.1109/TAC.2020.3047336. Preprint: arXiv:1912.08734.

[J6] Silun Zhang, Axel Ringh, Xiaoming Hu, and Johan Karlsson. Modeling collective behaviors: A moment-based approach. IEEE Transactions on Automatic Control, 66(1):33-48, 2021. https://doi.org/10.1109/TAC.2020.2976315. Preprint: arXiv:1804.02671.

[J5] Sebastian Banert, Axel Ringh, Jonas Adler, Johan Karlsson, and Ozan Öktem. Data-driven nonsmooth optimization. SIAM Journal on Optimization, 30(1):102–131, 2020. https://doi.org/10.1137/18M1207685. Preprint: arXiv:1808.00946.

[J4] Axel Ringh, Johan Karlsson, and Anders Lindquist. Multidimensional rational covariance extension with approximate covariance matching. SIAM Journal on Control and Optimization, 56(2):913–944, 2018. https://doi.org/10.1137/17M1127922. Preprint: arXiv:1704.08326.

[J3] Johan Karlsson and Axel Ringh. Generalized Sinkhorn iterations for regularizing inverse problems using optimal mass transport. SIAM Journal on Imaging Sciences, 10(4):1935–1962, 2017. https://doi.org/10.1137/17M111208X. Preprint: arXiv:1612.02273.

[J2] Axel Ringh, Johan Karlsson, and Anders Lindquist. Multidimensional rational covariance extension with applications to spectral estimation and image compression. SIAM Journal on Control and Optimization, 54(4):1950–1982, 2016. https://doi.org/10.1137/15M1043236. Preprint: arXiv:1507.01430.

[J1] Johan Karlsson, Anders Lindquist, and Axel Ringh. The multidimensional moment problem with complexity constraint. Integral equations and operator theory, 84(3):395–418, 2016. https://doi.org/10.1007/s00020-015-2248-z. Preprint: arXiv:1504.03626.

Short journal papers and other papers

[SJ3] Xin Mao, Li Qiu, Axel Ringh, and Dan Wang. Some counterexamples related to sectorial matrices and matrix phases. Examples and counterexamples, 1, 100019, 2021. https://doi.org/10.1016/j.exco.2021.100019.

[SJ2] Axel Ringh. On the sufficiency of K-positivity for truncated compactly supported generalized moment problems. Examples and counterexamples, 1, 100006, 2021. https://doi.org/10.1016/j.exco.2021.100006.

[SJ1] Yongxin Chen, Johan Karlsson, and Axel Ringh. Optimal transport for applications in control and estimation: An introduction to the special issue. IEEE Control Systems Magazine, 41(4), 28-33, 2021. https://doi.org/10.1109/MCS.2021.3076390.

Conference papers

[C11] Han Zhang, Axel Ringh, Weihan Jiang, Shaoyuan Li, and Xiaoming Hu. Statistically Consistent Inverse Optimal Control for Linear-Quadratic Tracking with Random Time Horizon. In 41st Chinese Control Conference, 2022. Received an Honorable mention of Guan Zhao-Zhi Award. Preprint: arXiv:2204.13013

[C10] Axel Ringh, Isabel Haasler, Yongxin Chen, and Johan Karlsson. Efficient computations of multi-species mean field games via graph-structured optimal transport. In 2021 60th IEEE Conference on Decision and Control (CDC), pages 5261-5268 . IEEE, 2021. https://doi.org/10.1109/CDC45484.2021.9682861. 

[C9] Axel Ringh, Johan Karlsson, and Anders Lindquist. On analytic interpolation with non-classical constraints for solving problems in robust control. In 2021 American Control Conference (ACC), pages 2374-2381. IEEE, 2021. https://doi.org/10.23919/ACC50511.2021.9483045. Preprint: arXiv:2010.14018.

[C8] Isabel Haasler, Axel Ringh, Yongxin Chen, and Johan Karlsson. Estimating ensemble flows on a hidden Markov chain. In 2019 IEEE 58th Conference on Decision and Control (CDC), pages 1331–1338. IEEE, 2019. https://doi.org/10.1109/CDC40024.2019.9029787. Preprint: arXiv:1905.09119.

[C7] Axel Ringh, Johan Karlsson, and Anders Lindquist. Lower bounds on the maximum delay margin by analytic interpolation. In 2018 IEEE Conference on Decision and Control (CDC), pages 5463–5469. IEEE, 2018. https://doi.org/10.1109/CDC.2018.8618930. Preprint: arXiv:1803.09487.

[C6] Silun Zhang, Axel Ringh, Xiaoming Hu, and Johan Karlsson. A moment-based approach to modeling collective behaviors. In 2018 IEEE Conference on Decision and Control (CDC), pages 1681–1687. IEEE, 2018. https://doi.org/10.1109/CDC.2018.8619389.

[C5] Axel Ringh, Johan Karlsson, and Anders Lindquist. Further results on multidimensional rational covariance extension with application to texture generation. In 2017 IEEE 56th Annual Conference on Decision and Control (CDC), pages 4038–4045. IEEE, 2017. https://doi.org/10.1109/CDC.2017.8264252.

[C4] Axel Ringh, Xiaodong Zhuge, Willem Jan Palenstijn, Kees Joost Batenburg, and Ozan Öktem. High-level algorithm prototyping: An example extending the TVR-DART algorithm. In International Conference on Discrete Geometry for Computer Imagery, pages 109–121. Springer, 2017. https://doi.org/10.1007/978-3-319-66272-5_10.

[C3] Axel Ringh, Johan Karlsson, and Anders Lindquist. The multidimensional circulant rational covariance extension problem: Solutions and applications in image compression. In 2015 54th IEEE Conference on Decision and Control (CDC), pages 5320–5327. IEEE, 2015. https://doi.org/10.1109/CDC.2015.7403052.

[C2] Axel Ringh and Johan Karlsson. A fast solver for the circulant rational covariance extension problem. In 2015 European Control Conference (ECC), pages 727–733. IEEE, 2015. Received the Best student paper prize. https://doi.org/10.1109/ECC.2015.7330629.

[C1] Axel Ringh and Anders Lindquist. Spectral estimation of periodic and skew periodic random signals and approximation of spectral densities. In Proceedings of the 33rd Chinese Control Conference, pages 5322–5327. IEEE, 2014. https://doi.org/10.1109/ChiCC.2014.6895847.

Technical reports

[TR1] Jonas Adler, Axel Ringh, Ozan Öktem, and Johan Karlsson. Learning to solve inverse problems using Wasserstein loss. arXiv:1710.10898, 2017. Presented at the workshop Optimal Transport and Machine Learning at the 2017 Conference on Neural Information Processing Systems (NeurIPS).

Thesis

[T2] Axel Ringh. Multidimensional inverse problems in imaging and identification using low-complexity models, optimal mass transport, and machine learning. Ph.D. thesis, KTH Royal Institute of Technology, 2018.

[T1] Axel Ringh. The Circulant Rational Covariance Extension Problem for a Skew Periodic Stochastic Process. M.Sc. thesis, KTH Royal Institute of Technology, 2014. Won Stockholms Matematikcentrum’s (SMC) prize for excellent Master thesis 2013/2014.