With numpy and scipy for matrix null space and LP.
- Go to https://software.seek.intel.com/performance-libraries
- Register
- Download Intel MKL
- tar zxvf l_mkl_2019.0.117.tgz
- cd folder
- ./install_GUI.sh
- Customize installation to install just Intel Math Kernel Library for C/C++ with GNU C/C++ compiler support
- cd ~/intel/mkl/bin$ && sh ./mklvars.sh intel64
- make clean: Clean build and exec.
- make: Compile
- make total: Clean, compile and run
- IMPORTANT:
- Check your Intel MKL and Python 3 path in the makefile.
.../intel/mkl/lib/intel64
must be in LD_LIBRARY_PATH (make total performs that).
Run run -?
to see help.
usage:
run options
where options are:
-n, --dimension <dimension> Problem dimension (integer) [Required]
-M, --matrix <matrix_path> Matrix file (string) [n!=3,5]
-t <dimacs_t> Dimacs t value (integer) [n!=3,5]
-D, --division <division_method> Division method (string) ['facet',
'bundfuss', 'zbund'] [Required]
-?, -h, --help display usage information
Remainder: Matrix dim 3 and dim 5 (Horn) is harcoded, run them using:
run -n 3 -D <division_method>
run -n 5 -D <division_method>
Division methods:
- facet: Facets and monotonicity (SARTECO18)
- bundfuss: Bundfuss
- zbund: Bundfuss and monotonicity