Forum / Newsgroup -> https://groups.google.com/forum/#!forum/opengm
Manual for OpenGM 2.0.2 -> http://hci.iwr.uni-heidelberg.de//opengm2/download/opengm-2.0.2-beta-manual.pdf
Code-Documentation for OpenGM 2.0.2 -> http://hci.iwr.uni-heidelberg.de//opengm2/doxygen/opengm-2.0.2-beta/html/index.html
OpenGM is a C++ template library for discrete factor graph models and distributive operations on these models. It includes state-of-the-art optimization and inference algorithms beyond message passing. OpenGM handles large models efficiently, since (i) functions that occur repeatedly need to be stored only once and (ii) when functions require different parametric or non-parametric encodings, multiple encodings can be used alongside each other, in the same model, using included and custom C++ code. No restrictions are imposed on the factor graph or the operations of the model. OpenGM is modular and extendible. Elementary data types can be chosen to maximize efficiency. The graphical model data structure, inference algorithms and different encodings of functions interoperate through well-defined interfaces. The binary OpenGM file format is based on the HDF5 standard and incorporates user extensions automatically.
Features
Factor Graph Models (Kschischang et al. 2001)
Graphs of any order and structure, from second order grid graphs to irregular higher-order models
Arbitrary (commutative and associative) operations, including sum, product, conjunction and disjunction
Flexible number of labels (different variables can have differently many labels)
Function sharing across factors
Function type abstraction. Different (built-in and custom) encodings can be used alongside each other
Functions
Explicit function (multi-dimensional table)
Sparse function (sparse multi-dimensional table)
Potts functions (different types, including higher-order)
Truncated absolute difference
Truncated squared difference
Views that treat one graphical model as a function within another graphical model
Algorithms
Loopy Belief Propagation (Pearl 1988, Yedidia et al. 2000)
parallel and sequential min-sum and max-product message passing (also for higher-order models)
message damping (Wainwright 2008)
Tree-reweighted Belief Propagation (TRBP) (Wainwright et al. 2005)
parallel and sequential min-sum and max-product message passing (also for higher-order models)
message damping (Wainwright 2008)
A-star branch-and-bound search (Bergtholdt et al. 2009)
Dual Decomposition
With sub-gradient methods (Kappes et al. 2010)
With bundle methods (Kappes et al. 2012)
Automated decomposition of arbitrary factor graphs
Arbitrary sub-solvers via templates
Graph Cut (Boykov et al. 2001).
Push-Relabel (Goldberg and Tarjan 1986)
Edmonds-Karp (Edmonds and Karp 1972)
Kolmogorov (Boykov and Kolmogorov 2004)
QPBO
MQPBO
Linear Programming Relaxations over the Local Polytope
TRWS
ADSAL
CombiLP
Integer Linear Programming
Multicut (Kappes et al. 2011)
Reduced-Inference (Kappes et al. 2013)
Alpha-Expansion
Alpha-Beta-Swap
Alpha-Fusion
Inf and Flip
Iterated Conditional Modes (ICM) (Besag 1986)
Lazy Flipper (Andres et al. 2010)
Kerninghan Lin
MCMC Metropolis-Hastings algorithms (Metropolis et al. 1953)
Gibbs sampling (Geman and Geman 1984)
Swendsen-Wang sampling (Swendsen and Wang 1987)
Wrappers around other graphical model libraries
MRF-LIB
LIB-DAI
TRW-S
QPBO
GCO
FastPD
AD3
DAOOPT
MPLP, MPLP-C
Binary HDF5 file format
Command Line Optimizer with built-in protocol mode for runtime and convergence analyses
Python Module with OpenGM C++ API exported to Python with boost::python
Allmost the complete C++ API is exported to Python
Allmost all C++ inference algorithms wrapped to Python
Vectorized API to add multiple functions and factors at once
Add functions via numpy ndarrays
Add functions via all default opengm function types
Extendibility through interfaces for
custom pure python function types
custom pure python visitor for inference
visualization of current inference state with matplotlib
Visualize Factor Graph (needs networkx and graphviz)
High performance
Graphical models with more than 10,000,000 factors
Specialized functions for optimized cache usage
Extendibility through interfaces for
custom algorithms
custom functions
custom label spaces
opengm/opengm - master
DerThorsten/opengm - master (opengm-python dev.)
Copyright (C) 2012 Bjoern Andres, Thorsten Beier and Joerg H.~Kappes.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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