Multi-matching optimization

Application and library to solve (multi-)graph matching problems.

For licensing term, see the LICENSE file. This work uses third party software. Please refer to LICENSE-3RD-PARTY.txt for an overview and recognition.

For details, refer to our publications:

M. Kahl, S. Stricker, L. Hutschenreiter, F. Bernard, C. Rother, B. Savchynskyy
“Optimizing Incomplete, Large-Scale and Sparse Multi-Graph Matching in Bioimaging”.
arXiv Pre-Print 2026. [PDF] [ArXiv]
M. Kahl, S. Stricker, L. Hutschenreiter, F. Bernard, C. Rother, B. Savchynskyy
“Towards Optimizing Large-Scale Multi-Graph Matching in Bioimaging”.
CVPR 2025. [PDF] [CVF Open Access]

Python

Install via pip

`pip install pylibmgm`

Refer to the usage guide: Python Documentation

C++

(Install requirements)
- Install g++ or clang (c++ compiler), meson (build system) and git
  - sudo apt install g++ meson git (Ubuntu)
  - sudo dnf install g++ meson git (Fedora)
Clone repository
- git clone https://github.com/vislearn/multi-matching-optimization.git
- cd ./multi-matching-optimization
Build
- meson setup --buildtype release -Db_ndebug=true ../builddir/
- meson compile -C ../builddir/
Run
- ../builddir/mgm -i tests/hotel_instance_1_nNodes_10_nGraphs_4.txt -o ../mgm_output --mode seqseq

Usage

Minimal Usage

$ mgm -i [IN_FILE] -o [OUT_DIR] --mode [OPT_MODE]

You can use one of the small models in the tests/ directory for testing.

Input files

Input files follow the .dd file format for multi-graph matching problems, as defined in the Structured prediction problem archive. See references below.

Available CLI parameters

--name
Base name for the resulting .json and .log files.
-l, --labeling
Path to an existing solution file (json). Pass to improve upon this labeling.
--mode ENUM
Optimization mode. See below.
--set-sizeINT
Subset size for incremenetal generation
--merge-one
In parallel local search, merge only the best solution. Do not try to merge other solutions as well.
-t, --threads INT
Number of threads to use. Upper limit defined by OMP_NUM_THREADS environment variable.
--libmpopt-seed UINT
Fix the random seed for the fusion moves graph matching solver of libmpopt.
--unary-constant FLOAT
Constant to add to every assignment cost. Negative values nudges matchings to be more complete.
--synchronize
Synchronize a cylce inconsistent solution. Excludes: --synchronize-infeasible
--synchronize-infeasible
Synchronize a cylce inconsistent solution. Allow all (forbidden) matchings. Excludes: --synchronize

Optimization modes

--mode specifies the optimization routine. It provides ready to use routines that combine construction, graph matching local search (GM-LS), and swap local search (SWAP-LS) algorithms as defined in the publication.

The following choices are currently available:

Construction modes. Use these to generate solutions quickly

seq: sequential construction
par: parallel construction
inc: incremental construction

Construction + GM-LS modes. A bit slower, but gives better solutions

seqseq: sequential construction -> sequential GM-LS
seqpar: sequential construction -> parallel GM-LS
parseq: parallel construction -> sequential GM-LS
parpar: parallel construction -> parallel GM-LS
incseq: incremental construction -> sequential GM-LS
incpar: incremental construction -> parallel GM-LS

Construction + iterative GM-LS & SWAP-LS. After construction, iterate between GM-LS and and SWAP-LS.

optimal: sequential construction -> Until conversion: (sequential GM-LS <-> swap local search)
optimalpar: parallel construction -> Until conversion: (parallel GM-LS <-> swap local search)

Improve given labeling.

Skip construction and perform local search on a pre-existing solution. Improve modes require a labeling to be provided via the --labeling [JSON_FILE] command line option.

improve-swap: improve with SWAP-LS
improve-qap: improve with sequential GM-LS
improve-qap-par: improve with parallel GM-LS
improveopt: improve with alternating sequential GM-LS <-> SWAP-LS
improveopt-par: improve with alternating parallel GM-LS <-> SWAP-LS

Use as synchronization algorithm

To synchronize a pre-existing cylce inconsistent solution, call with --synchonize or --synchonize-infeasible, either disallowing or allowing forbidden matchings.

Synchronize feasible. Feasible solution. Disallows forbidden matchings.

$ mgm -i [IN_FILE] -o [OUT_DIR] --mode [OPT_MODE] --labeling [JSON_FILE] --synchonize

Synchronize infeasible. Infeasible solution. Allows forbidden matchings.

$ mgm -i [IN_FILE] -o [OUT_DIR] --mode [OPT_MODE] --labeling [JSON_FILE] --synchonize-infeasible

Software dependencies

This software incorporates and uses third party software. Dependencies are provided as meson wrap files. They should be installed automatically during the build process, but manual installation can help solve issues, if meson fails to build them.

NOTE: Links given point to the original authors publications, which may have been substantially modified. The corresponding .wrap files in this repositry's subprojects/ folder contain links to the actual code needed to compile the solver.

spdlog
logging library; https://github.com/gabime/spdlog
cli11
Command line parser; https://github.com/CLIUtils/CLI11
nlohman_json
json parsing. https://github.com/nlohmann/json
unordered_dense
A fast & densely stored hashmap; https://github.com/martinus/unordered_dense
libqpbo
for quadratic pseudo boolean optimization (QPBO); https://pub.ista.ac.at/~vnk/software.html
libmpopt
for quadratic assignment problem (QAP) optimization; https://github.com/vislearn/libmpopt
Scipy_lap
Linear assignment problem (LAP) solver implementation from the Scipy python package; https://github.com/scipy/scipy

References

M. Kahl, S. Stricker, L. Hutschenreiter, F. Bernard, C. Rother, B. Savchynskyy
“Optimizing Incomplete, Large-Scale and Sparse Multi-Graph Matching in Bioimaging”.
arXiv Pre-Print 2026. [PDF] [ArXiv]
M. Kahl, S. Stricker, L. Hutschenreiter, F. Bernard, C. Rother, B. Savchynskyy
“Towards Optimizing Large-Scale Multi-Graph Matching in Bioimaging”.
CVPR 2025. [PDF] [CVF Open Access]
L. Hutschenreiter, S. Haller, L. Feineis, C. Rother, D. Kainmüller, B. Savchynskyy.
“Fusion Moves for Graph Matching”.
ICCV 2021. [PDF] [ArXiv]
Swoboda, P., Andres, B., Hornakova, A., Bernard, F., Irmai, J., Roetzer, P., Savchynskyy, B., Stein, D. and Abbas, A.
“Structured prediction problem archive”
arXiv preprint arXiv:2202.03574 (2022) [PDF] [ArXiv]

Metadata

Release files for pylibmgm 1.1.3

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Source distribution for pylibmgm 1.1.3
File	Size	Uploaded
pylibmgm-1.1.3.tar.gz	8.1 MB	Oct 5, 2026	Details

Built distributions (wheels)

Table of built distributions (wheels) for pylibmgm 1.1.3
File	Interpreter	ABI	Platform	Reset
pylibmgm-1.1.3-cp314-cp314t-win_amd64.whl 1.2 MB Oct 5, 2026	CPython 3.14	CPython 3.14 free-threading	Windows x86-64	Details
pylibmgm-1.1.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 1.3 MB Oct 5, 2026	CPython 3.14	CPython 3.14 free-threading	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
pylibmgm-1.1.3-cp314-cp314t-macosx_12_0_x86_64.whl 1.4 MB Oct 5, 2026	CPython 3.14	CPython 3.14 free-threading	macOS 12.0+ x86-64	Details
pylibmgm-1.1.3-cp314-cp314t-macosx_12_0_arm64.whl 1.3 MB Oct 5, 2026	CPython 3.14	CPython 3.14 free-threading	macOS 12.0+ ARM64	Details
pylibmgm-1.1.3-cp314-cp314-win_amd64.whl 1.2 MB Oct 5, 2026	CPython 3.14	CPython 3.14	Windows x86-64	Details
pylibmgm-1.1.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 1.3 MB Oct 5, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
pylibmgm-1.1.3-cp314-cp314-macosx_12_0_x86_64.whl 1.4 MB Oct 5, 2026	CPython 3.14	CPython 3.14	macOS 12.0+ x86-64	Details
pylibmgm-1.1.3-cp314-cp314-macosx_12_0_arm64.whl 1.3 MB Oct 5, 2026	CPython 3.14	CPython 3.14	macOS 12.0+ ARM64	Details
pylibmgm-1.1.3-cp313-cp313-win_amd64.whl 1.2 MB Oct 5, 2026	CPython 3.13	CPython 3.13	Windows x86-64	Details
pylibmgm-1.1.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 1.3 MB Oct 5, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
pylibmgm-1.1.3-cp313-cp313-macosx_12_0_x86_64.whl 1.4 MB Oct 5, 2026	CPython 3.13	CPython 3.13	macOS 12.0+ x86-64	Details
pylibmgm-1.1.3-cp313-cp313-macosx_12_0_arm64.whl 1.3 MB Oct 5, 2026	CPython 3.13	CPython 3.13	macOS 12.0+ ARM64	Details
pylibmgm-1.1.3-cp312-cp312-win_amd64.whl 1.2 MB Oct 5, 2026	CPython 3.12	CPython 3.12	Windows x86-64	Details
pylibmgm-1.1.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 1.3 MB Oct 5, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
pylibmgm-1.1.3-cp312-cp312-macosx_12_0_x86_64.whl 1.4 MB Oct 5, 2026	CPython 3.12	CPython 3.12	macOS 12.0+ x86-64	Details
pylibmgm-1.1.3-cp312-cp312-macosx_12_0_arm64.whl 1.3 MB Oct 5, 2026	CPython 3.12	CPython 3.12	macOS 12.0+ ARM64	Details
pylibmgm-1.1.3-cp311-cp311-win_amd64.whl 1.2 MB Oct 5, 2026	CPython 3.11	CPython 3.11	Windows x86-64	Details
pylibmgm-1.1.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 1.3 MB Oct 5, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64	Details
pylibmgm-1.1.3-cp311-cp311-macosx_12_0_x86_64.whl 1.4 MB Oct 5, 2026	CPython 3.11	CPython 3.11	macOS 12.0+ x86-64	Details
pylibmgm-1.1.3-cp311-cp311-macosx_12_0_arm64.whl 1.3 MB Oct 5, 2026	CPython 3.11	CPython 3.11	macOS 12.0+ ARM64	Details
pylibmgm-1.1.3-cp310-cp310-win_amd64.whl 1.2 MB Oct 5, 2026	CPython 3.10	CPython 3.10	Windows x86-64	Details
pylibmgm-1.1.3-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 1.3 MB Oct 5, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.27+ x86-64, Linux glibc 2.28+ x86-64	Details
pylibmgm-1.1.3-cp310-cp310-macosx_12_0_x86_64.whl 1.4 MB Oct 5, 2026	CPython 3.10	CPython 3.10	macOS 12.0+ x86-64	Details
pylibmgm-1.1.3-cp310-cp310-macosx_12_0_arm64.whl 1.3 MB Oct 5, 2026	CPython 3.10	CPython 3.10	macOS 12.0+ ARM64	Details

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Release files / pylibmgm-1.1.3-cp310-cp310-macosx_12_0_x86_64.whl

Download URL	pylibmgm-1.1.3-cp310-cp310-macosx_12_0_x86_64.whl
Size	1.4 MB
Tags	CPython 3.10 macOS 12.0+ x86-64
SHA-256 checksum How to use checksums	`3c957e5d30c233a1bbf361a62bfda61d1d312a13ff01b5a27d08880caf405d78`
BLAKE2b-256 checksum How to use checksums	`245fe93ddab96338e5dccaa8b74091e843f87e33a2fd2ea1da16162bc8e019c5`
Upload date	Oct 5, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/vislearn/multi-matching-optimization
Publishing commit: github.com/vislearn/multi-matching-optimization/tree/bd432ae5053c4fcf884728f7203810a9928bad03

Workflow

Publishing configuration: github.com/vislearn/multi-matching-optimization/blob/bd432ae5053c4fcf884728f7203810a9928bad03/.github/workflows/build_wheels_release.yml
Publishing logs: github.com/vislearn/multi-matching-optimization/actions/runs/37317295417/attempts/1

Artifact

Subject: pylibmgm-1.1.3-cp310-cp310-macosx_12_0_x86_64.whl
SHA-256 checksum: 3c957e5d30c233a1bbf361a62bfda61d1d312a13ff01b5a27d08880caf405d78 Verifying attestations

Release files / pylibmgm-1.1.3-cp310-cp310-macosx_12_0_arm64.whl

Download URL	pylibmgm-1.1.3-cp310-cp310-macosx_12_0_arm64.whl
Size	1.3 MB
Tags	CPython 3.10 macOS 12.0+ ARM64
SHA-256 checksum How to use checksums	`5b25689379cce68e11f0b48624df707a06eb0ef81de505a2315ef54f4cfba59b`
BLAKE2b-256 checksum How to use checksums	`41ef454e5b130bfd32802747d249b352420abeb7704baf697cd89e1553d4a132`
Upload date	Oct 5, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/vislearn/multi-matching-optimization
Publishing commit: github.com/vislearn/multi-matching-optimization/tree/bd432ae5053c4fcf884728f7203810a9928bad03

Workflow

Publishing configuration: github.com/vislearn/multi-matching-optimization/blob/bd432ae5053c4fcf884728f7203810a9928bad03/.github/workflows/build_wheels_release.yml
Publishing logs: github.com/vislearn/multi-matching-optimization/actions/runs/37317295417/attempts/1

Artifact

Subject: pylibmgm-1.1.3-cp310-cp310-macosx_12_0_arm64.whl
SHA-256 checksum: 5b25689379cce68e11f0b48624df707a06eb0ef81de505a2315ef54f4cfba59b Verifying attestations