Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields
DOI10.1137/21M1433241zbMath1502.90120arXiv2107.06028OpenAlexW3178113344MaRDI QIDQ5043730
Thomas Möllenhoff, Michael Moeller, Hartmut Bauermeister, Daniel Cremers, Emanuel Laude
Publication date: 6 October 2022
Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2107.06028
Markov random fieldsoptimal transportpolynomial optimizationsum of squaresmoment relaxationgeneralized conjugacy
Programming involving graphs or networks (90C35) Nonconvex programming, global optimization (90C26) Numerical optimization and variational techniques (65K10) Methods involving semicontinuity and convergence; relaxation (49J45) Optimal transportation (49Q22) Polynomial optimization (90C23)
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