A quadratic programming solver library written in C
The libqpsolver use Primal Interior Point Method to solve quadratic programming problems based on Chapter 9 to 11 of Convex Optimization by Stephen Boyd and Lieven Vandenberghe
- Support solving quadratic programming problems with or without equality, inequality, lower bound and upper bound constraints
- Based on Primal Interrior Point Method
- Support infeasible start with basic phase1 method (Not yet been fully tested)
- Based on Intel Math Kernel Libray (Planed to support OpenBLAS in the future)
- The solver is designed for solving small size problems (with optimization variable smaller than 50)
- The parameters need to be tweaked for the best performance and numerical stability depends on your own problems
- The solver did not handle input coefficients with extremely different order (reference to the problem)
Problems containing data with large and/or small coefficients, say 1.0e+9 or 1.0e-7 , are often hard to solve. Significant digits may be truncated in calculations with finite precision, which can result in the optimizer relying on inaccurate calculations. Since computers work in finite precision, extreme coefficients should be avoided. In general, data around the same “order of magnitude” is preferred, and we will refer to a problem, satisfying this loose property, as being well-scaled. If the problem is not well scaled, MOSEK will try to scale (multiply) constraints and variables by suitable constants. MOSEK solves the scaled problem to improve the numerical properties.
Intel Math Kernel Library (MKL)
Installation:
./mkl_install.sh
Make sure the MKL_PATH variable in config.mk is as same as your installation, the default directory path is set as:
MKL_PATH=/opt/intel/mkl
git clone https://github.com/shengwen-tw/libqpsolver.git
cd libqpsolver
make example
./example/qp_example
Dependencies
pip3 install setuptools numpy pybind11 progressbar
sudo apt install libsuitesparse-dev
git clone https://github.com/cvxopt/cvxopt.git
cd ./cvxopt
sudo python3 setup.py install
Run unit test of the linear algrebra functions
The program test the basic linear algebra functions underlying of the libqpsolver with randomly generated problems and compare the results with the Numpy. It is designed to run for several times (~250) and may take minutes to complete.
make unit_test_matrix
Run unit test of the quadratic programming solver
The unit test launchs the solver to solve randomly generated QP problems and compare the results (cost) with CVXOPT. It is designed to run for several times (~12000) and may take minutes to complete.
make unit_test_qp