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LHAC for Sparse inverse covariance Selection

by Xiaocheng Tang [https://mktal.github.io/]

LHAC for Sparse inverse covariance Selection, implements the algorithm LHAC -- Low rank Hessian Approximation in Active-set Coordinate descent (paper) -- for solving sparse inverse covariance selection problems, and recovers from a low-rank sample covariance matrix the inverse covariance matrix that is expected to have a sparse structure.

On use of LHAC for general composite minimization, please see here for more details.

Features

This package

  • handles sparse inverse covariance selections problems
  • supports various platforms, i.e., Mac OS X and Linux
  • supports both BLAS and CBLAS interfaces
  • includes a fast limited-memory BFGS library that can be used in general nonlinear optimizations

Citation

If you use LHAC in your research, please cite the following paper:

  • Katya Scheinberg and Xiaocheng Tang, Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis, Mathematical Programming Series A, 160(1), 495–529., 2016
@article{Scheinberg:2016wj,
  author = {Scheinberg, Katya and Tang, Xiaocheng},
  title = {{Practical inexact proximal quasi-Newton method with global complexity analysis}},
  journal = {Mathematical Programming},
  year = {2016},
  volume = {160},
  number = {1},
  pages = {495--529}
}

Build Guide

Download the package archive.

Extract the files:

tar xvf LHAC-SICS.zip
cd LHAC-SICS/src

LHAC comes with a MATLAB interface through MEX-files. To build the MEX-file on Linux, just run

mex -largeArrayDims sics_lhac.cpp sics_lhac-mex.cpp Lbfgs.cpp  -lmwblas -lmwlapack -lrt -output LHAC

Or if you are running Mac OS, you may compile the program using the provided Makefile (need to modify the first line to reflect where MATLAB is installed). Note that LHAC uses BLAS and LAPACK. The above command links to the BLAS and LAPACK library come with MATLAB, and the Makefile links to Apple's Accelerate framework that contains a version of BLAS and LAPACK optimized for Mac OS.

You will probably also need to modify the mexopts.sh in ~/.matlab before you run mex so that the compiler uses c++11 standard. To do that, simply replace the flag -ansi in CXXFLAG with -std=c++0x.

Usage Guide

After the MEX-file is compiled successfully, start MATLAB in the same folder and run LHAC_demo.m to verify the installation process. If successfully installed, LHAC_demo.m will produce outputs on the screen and upon completion returns the inverse covariance matrix in the variable named W.

Typical usage of LHAC is:

W = LHAC(S, lambda, Param);

where the solution W is the inverse covariance matrix recovered from the input S the sample covariance matrix, lambda is a positive scalar known as the regularization parameter and Param is a MATLAB struct that contains the algorithm parameters for LHAC. Some commonly-used parameters are listed below:

  • v: verbosity level 0-3 (default 2)
  • e: optimality tolerance (default 1e-6)
  • i: maximum number of iterations allowed (default 500)

Optionally, records of the optimization process, i.e., objective values, iteration counter, norm of the subgradient, etc., can be passed out to the output variable list besides the optimal solution:

[W, iter, fval, t, normgs, numActive] = LHAC(S, lambda, Param);

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Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis

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