Tim Hoheisel

According to our database, Tim Hoheisel authored at least 20 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Square Root LASSO: Well-Posedness, Lipschitz Stability, and the Tuning Trade-Off.

Aaron Berk, Simone Brugiapaglia, Tim Hoheisel

SIAM J. Optim., 2024

2023

LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing.

Aaron Berk, Simone Brugiapaglia, Tim Hoheisel

SIAM J. Math. Data Sci., December, 2023

From Perspective Maps to Epigraphical Projections.

Michael P. Friedlander, Ariel Goodwin, Tim Hoheisel

Math. Oper. Res., August, 2023

Uniqueness in Nuclear Norm Minimization: Flatness of the Nuclear Norm Sphere and Simultaneous Polarization.

Tim Hoheisel, Elliot Paquette

J. Optim. Theory Appl., April, 2023

Difference of submodular minimization via DC programming.

Marwa El Halabi, George Orfanides, Tim Hoheisel

Proceedings of the International Conference on Machine Learning, 2023

2022

A study of one-parameter regularization methods for mathematical programs with vanishing constraints.

Tim Hoheisel, Blanca Pablos, Aram-Alexandre Pooladian, Alexandra Schwartz, Luke Steverango

Optim. Methods Softw., 2022

Maximum Entropy on the Mean and the Cramér Rate Function in Statistical Estimation and Inverse Problems: Properties, Models, and Algorithms.

Yakov Vaisbourd, Rustum Choksi, Ariel Goodwin, Tim Hoheisel, Carola-Bibiane Schönlieb

CoRR, 2022

2021

Blind Deblurring of Barcodes via Kullback-Leibler Divergence.

Gabriel Rioux, Christopher Scarvelis, Rustum Choksi, Tim Hoheisel, Pierre Maréchal

IEEE Trans. Pattern Anal. Mach. Intell., 2021

A Study of Convex Convex-Composite Functions via Infimal Convolution with Applications.

James V. Burke, Tim Hoheisel, Quang V. Nguyen

Math. Oper. Res., 2021

2020

Maximum Entropy on the Mean: A Paradigm Shift for Regularization in Image Deblurring.

Gabriel Rioux, Rustum Choksi, Tim Hoheisel, Christopher Scarvelis

CoRR, 2020

A principled approach for generating adversarial images under non-smooth dissimilarity metrics.

Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel, Adam M. Oberman

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Variational Properties of Matrix Functions via the Generalized Matrix-Fractional Function.

James V. Burke, Yuan Gao, Tim Hoheisel

SIAM J. Optim., 2019

2018

Convex Geometry of the Generalized Matrix-Fractional Function.

James V. Burke, Yuan Gao, Tim Hoheisel

SIAM J. Optim., 2018

2015

Matrix Support Functionals for Inverse Problems, Regularization, and Learning.

James V. Burke, Tim Hoheisel

SIAM J. Optim., 2015

On a Smooth Dual Gap Function for a Class of Player Convex Generalized Nash Equilibrium Problems.

Nadja Harms, Tim Hoheisel, Christian Kanzow

J. Optim. Theory Appl., 2015

2014

On a Smooth Dual Gap Function for a Class of Quasi-Variational Inequalities.

Nadja Harms, Tim Hoheisel, Christian Kanzow

J. Optim. Theory Appl., 2014

2013

Epi-convergent Smoothing with Applications to Convex Composite Functions.

James V. Burke, Tim Hoheisel

SIAM J. Optim., 2013

Theoretical and numerical comparison of relaxation methods for mathematical programs with complementarity constraints.

Tim Hoheisel, Christian Kanzow, Alexandra Schwartz

Math. Program., 2013

A smoothing-regularization approach to mathematical programs with vanishing constraints.

Wolfgang Achtziger, Tim Hoheisel, Christian Kanzow

Comput. Optim. Appl., 2013

2012

Convergence of a local regularization approach for mathematical programmes with complementarity or vanishing constraints.

Tim Hoheisel, Christian Kanzow, Alexandra Schwartz

Optim. Methods Softw., 2012