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Mathematical Optimization Group

Department of Mathematics, University of Tübingen, Germany

Publications

Journal 2022

M.C. Mukkamala, J. Fadili, P. Ochs:
Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms. [pdf]
Journal of Global Optimization, 83:753-781, 2022.

Others 2022

S. Mehmood, P. Ochs:
Fixed-Point Automatic Differentiation of Forward--Backward Splitting Algorithms for Partly Smooth Functions. [pdf]
Technical Report, ArXiv e-prints, arXiv:2208.03107, 2022.

S. Wang, J. Fadili, P. Ochs:
Inertial Quasi-Newton Methods for Monotone Inclusion: Efficient Resolvent Calculus and Primal-Dual Methods. [pdf]
Technical Report, ArXiv e-prints, arXiv:2209.14019, 2022.

M. Sucker, P. Ochs:
PAC-Bayesian Learning of Optimization Algorithms. [pdf]
Technical Report, ArXiv e-prints, arXiv:2210.11113, 2022.

Conference 2021

S. Mehmood, P. Ochs:
Differentiating the Value Function by using Convex Duality. [pdf]
International Conference on Artificial Intelligence and Statistics, 2021.

M.C. Mukkamala, F. Westerkamp, E. Laude, D. Cremers, P. Ochs:
Bregman Proximal Framework for Deep Linear Neural Networks. [pdf]
International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). Lecture Notes in Computer Science, Springer, 2021.

Others 2021

S. Bonettini, P. Ochs, M. Prato, S. Rebegoldi:
An Abstract Convergence Framework with Application to Inertial Inexact Forward-Backward Methods. [pdf]
Technical Report, Preprint at Optimization Online (8534), 2021.

Journal 2020

E. Laude, P. Ochs, D. Cremers:
Bregman Proximal Mappings and Bregman-Moreau Envelopes under Relative Prox-Regularity. [pdf]
Journal of Optimization Theory and Applications, 184(3):724-761, 2020.

M.C. Mukkamala, P. Ochs, T. Pock, S. Sabach:
Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Non-Convex Optimization. [pdf]
SIAM Journal on Mathematics of Data Science, 2(3):658-682, 2020.

Conference 2020

S. Mehmood, P. Ochs:
Automatic Differentiation of Some First-Order Methods in Parametric Optimization. [pdf]
International Conference on Artificial Intelligence and Statistics, 2020.

Others 2020

M.C. Mukkamala, J. Fadili, P. Ochs:
Global Convergence of Model Function Based Bregman Proximal Minimization Algorithms. [pdf]
Technical Report, ArXiv e-prints, arXiv:2012.13161, 2020.

Journal 2019

P. Ochs:
Unifying abstract inexact convergence theorems and block coordinate variable metric iPiano. [pdf]
SIAM Journal on Optimization, 29(1):541-570, 2019.

P. Ochs, T. Pock:
Adaptive Fista for Non-convex Optimization. [pdf]
SIAM Journal on Optimization, 29(4):2482-2503, 2019.

S. Becker, J. Fadili, P. Ochs:
On Quasi-Newton Forward--Backward Splitting: Proximal Calculus and Convergence. [pdf]
SIAM Journal on Optimization, 29(4):2445-2481, 2019.

Conference 2019

Y. Malitsky, P. Ochs:
Model Function Based Conditional Gradient Method with Armijo-like Line Search. [pdf]
In K. Chaudhuri, R. Salakhutdinov (Eds.): International Conference on Machine Learning (ICML). Proceedings of Machine Learning Research, Vol. 97, 4891-4900, PMLR, 2019.

J.A. Tomasson, P. Ochs, J. Weickert:
AFSI: Adaptive restart for fast semi-iterative schemes for convex optimisation. [pdf]
In T. Brox, A. Bruhn, M. Fritz (Eds.): German Conference on Pattern Recognition (GCPR). Lecture Notes in Computer Science, Vol. 11269, 669-681, Springer, 2019.

M.C. Mukkamala, P. Ochs:
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms. [pdf]
Conference on Neural Information Processing Systems (NeurIPS), 2019.

Journal 2018

P. Ochs:
Local Convergence of the Heavy-ball Method and iPiano for Non-convex Optimization. [pdf]
Journal of Optimization Theory and Applications, 177(1):153-180, 2018.

P. Ochs, J. Fadili, T. Brox:
Non-smooth Non-convex Bregman Minimization: Unification and new Algorithms. [pdf]
Journal of Optimization Theory and Applications, 181(1):244-278, 2018.

Conference 2018

P. Ochs, T. Meinhardt, L. Leal-Taixe, M. Moeller:
Lifting Layers: Analysis and Applications. [pdf]
In V. Ferrari, M. Hebert, C. Sminchisescu, Y. Weiss (Eds.): European Conference on Computer Vision (ECCV). Springer International Publishing, 2018. (Oral Presentation at ECCV18)

Others 2018

S. Becker, J. Fadili, P. Ochs:
On Quasi-Newton Forward--Backward Splitting: Proximal Calculus and Convergence. [pdf]
Technical Report, ArXiv e-prints, arXiv:1801.08691 [math.OC], 2018.

Others 2017

P. Ochs, J. Fadili, T. Brox:
Non-smooth Non-convex Bregman Minimization: Unification and new Algorithms. [pdf]
Technical Report, ArXiv e-prints, arXiv:1707.02278 [math.OC], 2017.

P. Ochs, T. Pock:
Adaptive Fista. [pdf]
Technical Report, ArXiv e-prints, arXiv:1711.04343 [math.OC], 2017.

Journal 2016

P. Ochs, R. Ranftl, T. Brox, T. Pock:
Techniques for gradient based bilevel optimization with nonsmooth lower level problems. [pdf]
Journal of Mathematical Imaging and Vision, 56(2):175-194, 2016. (Invited Paper)

Conference 2016

S. Müller, P. Ochs, J. Weickert, N. Graf:
Robust interactive multi-label segmentation with an advanced edge detector. [pdf]
In B. Andres, B. Rosenhahn (Eds.): German Conference on Pattern Recognition (GCPR). Lecture Notes in Computer Science, Vol. 9796, 117-128, Springer, 2016.

D. Hafner, P. Ochs, J. Weickert, M. Reißel, S. Grewenig:
FSI schemes: Fast semi-iterative solvers for PDEs and Optimisation Methods. [pdf]
In B. Andres, B. Rosenhahn (Eds.): German Conference on Pattern Recognition (GCPR). Lecture Notes in Computer Science, Vol. 9796, 91-102, Springer, 2016. (Best Paper Award)

Others 2016

P. Ochs:
Local Convergence of the Heavy-ball Method and iPiano for Non-convex Optimization. [pdf]
Technical Report, ArXiv e-prints, arXiv:1606.09070 [math.OC], 2016.

P. Ochs:
Unifying abstract inexact convergence theorems and block coordinate variable metric iPiano. [pdf]
Technical Report, ArXiv e-prints, arXiv:1602.07283 [math.OC], 2016.

Journal 2015

P. Ochs, T. Brox, T. Pock:
iPiasco: Inertial Proximal Algorithm for strongly convex Optimization. [pdf]
Journal of Mathematical Imaging and Vision, 53(2):171-181, 2015.

P. Ochs, A. Dosovitskiy, T. Brox, T. Pock:
On iteratively reweighted algorithms for non-smooth non-convex optimization in computer vision. [pdf]
SIAM Journal on Imaging Sciences, 8(1):331-372, 2015.

Conference 2015

P. Ochs, R. Ranftl, T. Brox, T. Pock:
Bilevel Optimization with Nonsmooth Lower Level Problems. [pdf]
In J.-F. Aujol, M. Nikolova, N. Papadakis (Eds.): International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). Lecture Notes in Computer Science, Vol. 9087, 654-665, Springer, 2015. (Best Paper Award)

Others 2015

P. Ochs:
Long Term Motion Analysis for Object Level Grouping and Nonsmooth Optimization Methods. [pdf]
PhD Thesis, Albert-Ludwigs-Universität Freiburg, 2015.

Journal 2014

P. Ochs, J. Malik, T. Brox:
Segmentation of moving objects by long term video analysis. [pdf]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(6):1187-1200, 2014.

P. Ochs, Y. Chen, T. Brox, T. Pock:
iPiano: Inertial Proximal Algorithm for Non-convex Optimization. [pdf]
SIAM Journal on Imaging Sciences, 7(2):1388-1419, 2014.

Conference 2013

P. Ochs, A. Dosovitskiy, T. Pock, T. Brox:
An iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision. [pdf]
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.

Journal 2012

M. Temerinac-Ott, O. Ronneberger, P. Ochs, W. Driever, T. Brox, H. Burkhardt:
Multiview deblurring for 3-D images from light sheet based fluorescence microscopy.
IEEE Transactions on Image Processing, 21(4):1863-1873, 2012.

Conference 2012

N.S. Nagaraja, P. Ochs, K. Liu, T. Brox:
Hierarchy of Localized Random Forests for Video Annotation. [pdf]
In A. Pinz, T. Pock, H. Bischof, F. Leberl (Eds.): Pattern Recognition (Proc. DAGM). Lecture Notes in Computer Science, Vol. 7476, Springer, 2012.

P. Ochs, T. Brox:
Higher Order Motion Models and Spectral Clustering. [pdf]
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2012.

Conference 2011

P. Ochs, T. Brox:
Object segmentation in video: a hierarchical variational approach for turning point trajectories into dense regions. [pdf]
IEEE International Conference on Computer Vision (ICCV), 2011.



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