lincon
a quadratic programming solver with support for linear equality and inequality constraints, i.e.
minimize (1/2)x^TQx + q^Tx
subject to Cx = c, Dx <= d
convergence rate: O (1/k^2)
references
documentation: https://mirca.github.io/lincon
Instalation
C++
just copy the file lincon/src/solver.h =)
Python
pip install lincon
Basic usage
C++
#include "solver.h"
#include <iostream>
using namespace Eigen;
using namespace std;
int main() {
Eigen::MatrixXd Qmat(2, 2), Dmat(2, 2);
Eigen::MatrixXd Cmat(1, 2);
Eigen::VectorXd qvec(2), cvec(1), dvec(2), w0(2);
Qmat << 2.,.5, .5,1.;
w0 << .5, .5;
qvec << 1.,1.;
Cmat << 1.,1.;
Dmat << -1.,0., 0.,-1.;
cvec << 1.;
dvec << 0.,0.;
qpsolver* my_solver = new qpsolver(Qmat, qvec, Cmat, cvec, Dmat, dvec, w0);
std::cout << my_solver->solve() << std::endl;
}
Python
import numpy as np
from lincon import qp
Qmat = np.array([[2., .5], [.5, 1]], dtype=np.float64)
w0 = .5 * np.ones(2, dtype=np.float64)
qvec = np.ones(2, dtype=np.float64)
Cmat = np.atleast_2d(np.ones(2, dtype=np.float64))
Dmat = np.array([[-1, 0], [0, -1]], dtype=np.float64)
cvec = np.ones(1, dtype=np.float64)
dvec = np.zeros(2, dtype=np.float64)
x = qp.solve(Qmat=Qmat, qvec=qvec, Cmat=Cmat, cvec=cvec,
Dmat=Dmat, dvec=dvec, w0=w0)
License
Copyright 2019 Ze Vinicius
This project is licensed under the terms of the MIT License.
