LP_MP is a C++ framework for developing scalable dual (Lagrangean) decomposition based algorithms solvers for a wide range of LP-relaxations to discrete optimization problems. For a theoretical introduction to the techniques used and the class of problems that can be optimized see [1].
Solvers are provided in separate projects and include
Optimization techniques include
- Messsage passing [1],
- Subgradient ascent with a proximal bundle method based on the Frank-Wolfe algorithm [2], Vladimir Kolmogorov's original implementation.
- An interface to external solvers is provided by DD_ILP.
Type git clone https://github.com/pawelswoboda/LP_MP.git for downloading, then cd LP_MP and git submodule update --init for downloading dependenciesand finallycmake` for building.
Prerequisites:
- Clang 5.0 or GCC 7.0 upwards for C++17 compatibility.
- [1]:
P. Swoboda, J. Kuske and B. Savchynskyy. A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems. In CVPR 2017. - [2]:
P. Swoboda and V. Kolmogorov. MAP inference via Block-Coordinate Frank-Wolfe Algorithm. arXiv.