Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields (bibtex)
by H Bauermeister, E Laude, T Möllenhoff, M Möller and D Cremers
Reference:
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields (H Bauermeister, E Laude, T Möllenhoff, M Möller and D Cremers), In SIAM J. Imaging Sci., volume 15, 2022. 
Bibtex Entry:
@article{laude2021lifting,
 title = {Lifting the Convex Conjugate in Lagrangian Relaxations: {A} Tractable
                  Approach for Continuous Markov Random Fields},
 author = {H Bauermeister and E Laude and T Möllenhoff and M Möller and D Cremers},
 journal = {{SIAM} J. Imaging Sci.},
 volume = {15},
 number = {3},
 pages = {1253--1281},
 year = {2022},
 keywords = {Markov random fields, moment relaxation, sum of squares, polynomial optimization, generalized conjugacy, optimal transport},
}
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Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields (bibtex)
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields (bibtex)
by H Bauermeister, E Laude, T Möllenhoff, M Möller and D Cremers
Reference:
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields (H Bauermeister, E Laude, T Möllenhoff, M Möller and D Cremers), In SIAM J. Imaging Sci., volume 15, 2022. 
Bibtex Entry:
@article{laude2021lifting,
 title = {Lifting the Convex Conjugate in Lagrangian Relaxations: {A} Tractable
                  Approach for Continuous Markov Random Fields},
 author = {H Bauermeister and E Laude and T Möllenhoff and M Möller and D Cremers},
 journal = {{SIAM} J. Imaging Sci.},
 volume = {15},
 number = {3},
 pages = {1253--1281},
 year = {2022},
 keywords = {Markov random fields, moment relaxation, sum of squares, polynomial optimization, generalized conjugacy, optimal transport},
}
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members:laudee

Table of Contents

Research Interests

Convex and Nonconvex Optimization for Machine Learning and Computer Vision, Convex Relaxation Methods

Bio

I'm a PhD student in computer science at the Computer Vision Group TUM headed by Prof. Daniel Cremers. In my research I focus on Numerical Optimization for Machine Learning and Computer Vision and Convex Relaxation Methods.

I received my Bachelor's degree in Computer Science from the University of Würzburg in 2013 and my Master's degree in Informatics (minor Mathematics) in 2015 from the Technical University of Munich.

Publications


Teaching

Winter Term 2019/20

Summer Term 2018

Winter Term 2017/18

Summer Term 2017

Summer Term 2016