Implementation of Persistent Homology Localization Algorithms
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Persistent Homology Localization Algorithms

Copyright 2017 Rutgers University and CUNY Queens College


This page contains the code of persistent homology localization algorithm proposed in [1]. For a given homology class in a certain complex, this code computes the optimal (shortest) representative cycle. Here the shortest cycle refers to the cycle with least amount of edges on unweighted complex. Extension to weighted complex is straightforward.

Currently, there are three types of input data that are supported by this software package:

  1. d-dimensional gray-scale image data. This data is internally discretized into cubical complex.
  2. Distance matrix data. This data is internally interpreted as a Vietoris-Rips complex of as many points as there are columns in the given matrix. The distance between any two points is given by the input matrix data.
  3. General simplical complex.

The output produced by this software consists of the optimal representative cycles along with the persistence diagram data.

The input and output file formats are specified below. This software package includes Matlab functions to create the input files and interpret output results.


  1. Windows:

    • This code has been tested on Visual Studio 2015. Since features of C++11 are utilized in this code, older C++ compilers are not supported (e.g., Visual Studio 2013 will not compile successfully).

    • To compile, first need to include the blitz library: /Third_Party/. To avoid the deprecation warnings, add _CRT_SECURE_NO_WARNINGS and _SCL_SECURE_NO_WARNINGS to the compile options, which can be set in Project Properties -> C/C++ -> Preprocessor -> Preprocessor Definitions.

  2. Linux/macOS:

    • This code has been tested on G++ version 4.8.4, and any version after G++ 4.8.1 should also work (since GCC 4.8.1 has a complete concurrency support). To install latest gcc/g++ compiler on Ubuntu, please follow the tutorial: How to install latest gcc on Ubuntu LTS.
    • To compile, just use the Makefile provided.

To ease the pain of compiling the codes, precompiled executables are also provided in the release branch. For Windows executables, they were generated on Windows 8.1. For Linux executable, it was generated on Ubuntu 14.04.


In command line, run: HomologyLocalization -f data_file_name [options]. Here, data_file_name is the name of input data, which can be simplicial complex, Vietoris-Rips complex or cubical complex. The available options are:

  • -t or --threshold: only homology classes with persistence greater than this threshold parameter will be considered for the computation of optimal cycles.
  • -a or --algorithm: specify which algorithm to employ to find the optimal representative cycles. Currently there are two options: 0, A* search; 1, classical exhaustive search on the whole covering graph. For instance, the following command HomologyLocalization -f data_file_name -a 0 will apply the A* search algorithm.
  • -d or --dimension: specify the maximum dimension to be considered for computation. For example, by default d=2, and this means we will only compute the 1d and 2d boundary matrices. Since this code is based on blitz library and the C++ template does not allow dynamic compilation, this software only supports maximum dimension of 8. To adapt it to different needs, just change one line of code in function runPersistenceHomology from InputRunner.h.
  • -p or --pthread: specify the number of threads for the computation of edge annotations. By default, it is 8.
  • -h or --help: show help information.

If no options are offered, the program will not run the cycle optimization algorithm and will only naively reduce the boundary matrix. As a result, it just returns the possibly non-optimal cycles.

File Formats:

For the input data, currently there are three file types that are supported.

  1. d-dimensional gray-scale image data (interpreted as cubical complex). A simple Matlab file writer is provided in this software package /Matlab/Save_Cubical_Image.m
    • File type, which is 0 for cubical complex data.
    • Data dimension, which is followed by the exact size in each dimension. For example, for a 2D image of size 400x400, we have 2 400 400.
    • Pixel values.
  2. Vietoris-Rips complex. A simple Matlab file writer and demo can be found in /Matlab/Save_Dense_Distance_Matrix.m and /Matlab/Demo_Full_Rips.m.
    • File type, which is 1 for dense distance matrix data.
    • Total number of points.
    • The dimension of each point. For instance, for points in 3D space, this number should be 3.
    • The positions of points.
    • Dense distance matrix.
  3. Simplicial complex. A simple Matlab file writer and demo are provided in /Matlab/Save_General_Simplicial_Complex.m and /Matlab/Demo_General_Simplicial_Complex.m.
    • File type, which is 2 for simplicial complex.
    • Maximum dimension of simplicial complex.
    • Total number of vertices.
    • The dimension of each vertex.
    • Positions of vertices and their corresponding filtration values.
    • Write indices of edges, faces, etc ..., just like adjacency matrix.

For the output, there are also three different kinds of file types: .pers, .red and .bnd. .pers stores the information of birth time and death time for each homology class, while .red and .bnd store the reduction process and the reduced boundary matrix, respectively. The number appended to the file extension .bnd indicates the dimension of the reduced boundary matrix, and this is similar for .red. For example, file_name.bnd.1 is for 1D reduced boundary matrix. The corresponding file readers are implemented in /Matlab/Read_Pers_Results_Cubical.m, /Matlab/Read_Pers_Results_FullRips.m and /Matlab/Read_Pers_Results_General_SimComplex.m.


  • Command HomologyLocalization -f filename.dat -a 1 -t 100 will compute the optimal cycles for homology class with persistence greater than 100, using classical exhaustive search.
  • Command HomologyLocalization -f filename.dat just performs the column-wise Gaussian reduction for the boundary matrix, and returns the possibly non-optimal cycles.


  • Fine-grained parallelism for the computation of edge annotations.

License and Disclaimer:

Currently released under GPLv3 (

The SOFTWARE PACKAGE provided in this page is provided "as is", without any guarantee made as to its suitability or fitness for any particular use. It may contain bugs, so use of this tool is at your own risk. We take no responsibility for any damage of any sort that may unintentionally be caused through its use.


If you find this code helpful, please cite our work [1] with the following bibtex:

	author = {Pengxiang Wu and 
           	Chao Chen and
           	Yusu Wang and
           	Shaoting Zhang and
           	Changhe Yuan and
           	Zhen Qian and
           	Dimitris N. Metaxas and
           	Leon Axel},
	title = {Optimal Topological Cycles and Their Application in Cardiac Trabeculae Restoration},
	booktitle = {Information Processing in Medical Imaging, {IPMI}},
	pages = {80--92},
	year = {2017}


If you have any questions regarding this code, please contact Pengxiang Wu (, or just leave a message below with Github (log-in is needed). Bug reports are quite welcome.


[1] P. Wu, C. Chen, Y. Wang, S. Zhang, C. Yuan, Z. Qian, D. Metaxas and L. Axel. "Optimal Topological Cycles and Their Application in Cardiac Trabeculae Restoration." In International Conference on Information Processing in Medical Imaging (IPMI), 2017.