@conference {Tourani2020,
	title = {Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization},
	booktitle = {AISTATS 2020},
	year = {2020},
	abstract = {We consider the maximum-a-posteriori inference problem in discrete graphical models and study solvers based on the dual block-coordinate ascent rule. We map all existing solvers in a single framework, allowing for a better understanding of their design principles. We theoretically show that some block-optimizing updates are sub-optimal and how to strictly improve them. On a wide range of problem instances of varying graph connec-tivity, we study the performance of existing solvers as well as new variants that can be obtained within the framework. As a result of this exploration we build a new state-of-the art solver, performing uniformly better on the whole range of test instances.},
	url = {https://gitlab.com/},
	author = {Tourani, Siddharth and Shekhovtsov, Alexander and Carsten Rother and Savchynskyy, Bogdan}
}
@conference {Tourani2018,
	title = {MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11208 LNCS},
	year = {2018},
	pages = {264{\textendash}281},
	abstract = {Dense, discrete Graphical Models with pairwise potentials are a powerful class of models which are employed in state-of-the-art computer vision and bio-imaging applications. This work introduces a new MAP-solver, based on the popular Dual Block-Coordinate Ascent principle. Surprisingly, by making a small change to a low-performing solver, the Max Product Linear Programming (MPLP) algorithm [7], we derive the new solver MPLP++ that significantly outperforms all existing solvers by a large margin, including the state-of-the-art solver Tree-Reweighted Sequential (TRW-S) message-passing algorithm [17]. Additionally, our solver is highly parallel, in contrast to TRW-S, which gives a further boost in performance with the proposed GPU and multi-thread CPU implementations. We verify the superiority of our algorithm on dense problems from publicly available benchmarks as well as a new benchmark for 6D Object Pose estimation. We also provide an ablation study with respect to graph density.},
	keywords = {Block-Coordinate-Ascent, graphical models, Message passing algorithms},
	isbn = {9783030012243},
	issn = {16113349},
	doi = {10.1007/978-3-030-01225-0_16},
	author = {Tourani, Siddharth and Shekhovtsov, Alexander and Carsten Rother and Savchynskyy, Bogdan}
}
