Tim Hoheisel, McGill University  Assistant Professor in Continuous Optimization
Address
805 Sherbrooke St West, Room 1114
Montréal, Québec, Canada H3A 0B9
Tel: 5143983807
tim.hoheisel@mcgill.ca
Research interests
My research lies at the intersection of continuous optimization and nonsmooth analysis and therefore between applied and pure mathematics. Hence, the problems on which I work can be motivated by a concrete application but also of purely conceptual interest.
CRM Applied Math seminar
Together with Simone Brugiapaglia (Concordia) I am organizing the CRM Applied Math seminar hosted at McGill.
Memeberships
 I am a core member of the MTL MLOpt group which is an assembly of researchers from the Montreal area working at the interface of optimization and machine learning.
 I am a member of SIAM (Society of Industrial and Applied Mathematics).
Some recent presentations
Students and Postdocs
Current
 1/2021: George Orfanides, PhD student (cosupervision with Adam Oberman).
 10/2020: Armand Gissler, Intern, ENS ParisSaclay
Past
 5/20199/2020: Gabriel Rioux, Masters student (cosupervision with R. Choksi). MSc. Thesis title: The Maximum Entropy on the Mean Method for Image Deblurring: Applying FenchelRockafellar Duality in Finite and Infinite Dimensions. Currently a PhD student at Cornell.
 9/20188/2020 : Quang Van Nguyen, Postdoc.
 5/20188/2020 : AramAlexandre Pooladian, Masters student (cosupervision with A. Oberman). MSc. Thesis title: Numerical Methods for the FermatWeber Problem in the polyhedral l_p norms. Currently a PhD student at NYU.
 9/20185/2020 : George Orfanides, MSc. Thesis title: A SmoothingRegularization Method for Mathematical Programs with Cardinality Constraints. After an internship at the Basque Center of Applied Mathematics (BCAM), Bilbao, he is now a PhD student.
Teaching
 Winter 2021
 MATH 247  Honours Applied Linear Algebra
 Fall 2020
 MATH 417/517  (Honours) Linear Optimization
 Winter 2020
 MATH 247  Honours Applied Linear Algebra
 Winter 2019
 MATH 247  Honours Applied Linear Algebra
 Fall 2018
 MATH 597  Topics in Applied Math (Convex Optimization)
Publications
Submitted for Publication
Journal Articles

G. Rioux, R. Choksi, T. Hoheisel, C. Scarvelis, and P. Maréchal: The Maximum Entropy on the Mean Method for Image Deblurring. Inverse Problems 37, 2021 (29 pp.).

G. Rioux, C. Scarvelis, R. Choksi, T. Hoheisel, and P. Maréchal: Blind Deblurring of Barcodes via KullbackLeibler Divergence. IEEE Transactions on Pattern Analysis and Machine Intelligence 43(1), 2021, pp.7788.
 T. Hoheisel, M. Laborde, and A. Oberman: A regularization interpretation of the proximal point method for weakly convex functions. Journal of Dynamics and Games 7(1), 2020, pp. 7996.
 J. V. Burke, Y. Gao, and T. Hoheisel: Variational properties of matrix functions via the
generalized matrixfractional function. SIAM Journal on Optimization 29(3), 2019, pp. 19581987.
 J. V. Burke, Y. Gao, and T. Hoheisel: Convex geometry of the generalized matrixfractional function. SIAM Journal on Optimization 28(3), 2018, pp. 21892200.
 J. V. Burke and T. Hoheisel: Epiconvergence properties of smoothing by infimal
convolution. Setvalued and Variational Analysis 25(1), 2017, pp. 123.
 J. V. Burke and T. Hoheisel: Matrix support functionals for inverse problems, regularization, and learning. SIAM Journal on Optimization 25(2), 2015, pp. 11351159.
 Nadja Harms, Tim Hoheisel, and Christian Kanzow: On a smooth dual gap function for a class of player convex generalized Nash equilibrium problems. Journal of Optimization Theory and Applications 166(2), 2015, pp. 659–685.
 N. Harms, T. Hoheisel, and C. Kanzow: On a Smooth Dual Gap Function for a Class of QuasiVariational Inequalities.
Journal of Optimization Theory and Applications 163, 2014, pp. 413438.

J. V. Burke and T. Hoheisel: Epiconvergent smoothing with applications to convex composite functions. SIAM Journal on Optimization 23(3), 2013, pp. 14571479.

J. V. Burke, T. Hoheisel, and C. Kanzow: Gradient consistency for integralconvolution smoothing functions. Setvalued and Variational Analysis 21(2), 2013, pp. 359376.

W. Achtziger, T. Hoheisel and C. Kanzow: A smoothingregularization approach
to mathematical programs with vanishing constraints. Computational Optimization and Applications 55(3), 2013, pp. 733767.

T. Hoheisel, C. Kanzow, and A. Schwartz: Theoretical and Numerical Comparison of Relaxation Methods for Mathematical Programs with Complementarity Constraints. Mathematical Programming 137, 2013, pp. 257288.

W. Achtziger, C. Kanzow, and T. Hoheisel: On a relaxation method for mathematical programs with vanishing constraints. GAMMMitteilungen 35, 2012, pp. 110130.

T. Hoheisel, C. Kanzow, and A. Schwartz: Mathematical Programs with Vanishing Constraints: A New Regularization Approach with Strong Convergence Properties. Optimization 61(6), 2012, pp. 619636.

T. Hoheisel, C. Kanzow, B. S. Mordukhovich, and H. Phan: Generalized Newton's Method Based on Graphical Derivatives. Nonlinear Analysis Series A: Theory, Methods, and Applications 75(3), 2012, pp. 13241340.

T. Hoheisel, C. Kanzow, and A. Schwartz: Convergence of a local regularization approach for mathematical programs with complementarity or vanishing constraints. Optimization Methods and Software 27(3), 2012, 483512.

T. Hoheisel, C. Kanzow, and A. Schwartz: Improved Convergence Properties of the LinFukushimaRegularization Method for Mathematical Programs with Complementarity Constraints. Numerical Algebra, Control, and Optimization 1(1), 2011, pp.4960.

T. Hoheisel, C. Kanzow and J. Outrata: Exact penalty results for mathematical programs with vanishing constraints. Nonlinear Analysis: Theory, Methods, and Applications 72, 2010, 25142526.

T. Hoheisel and C. Kanzow:
On the Abadie and Guignard Constraint
Qualification for Mathematical Programs with Vanishing Constraints,
Optimization 58, Issue 4, 2009, pp. 431  448.

T. Hoheisel and C. Kanzow:
Stationary Conditions for Mathematical Programs with Vanishing Constraints Using Weak
Constraint Qualifications, Journal of Mathematical Analysis and Applications 337,
2008, pp. 292310.

T. Hoheisel and C. Kanzow:
First and SecondOrder Optimality Conditions for Mathematical Programs
with Vanishing Constraints,
Applications of Mathematics 52, 2007, pp. 495514 (special issue dedicated to J.V. Outrata's 60th birthday).
Conference Proceedings
 A. Pooladian, C. Finlay, T. Hoheisel, and A. Oberman: A principled approach to generating adversarial attacks under nonsmooth dissimilarity metrics, Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
Book reviews
Thesis
Other publications