コード例 #1
0
bool pinv_damped(const MatrixD &A, MatrixD *invA, Scalar lambda_max, Scalar eps) {

  //A (m x n) usually comes from a redundant task jacobian, therfore we consider m<n
  int m = A.rows() - 1;
  VectorD sigma;  //vector of singular values
  Scalar lambda2;
  int r = 0;

  JacobiSVD<MatrixD> svd_A(A.transpose(), ComputeThinU | ComputeThinV);
  sigma = svd_A.singularValues();
  if (((m > 0) && (sigma(m) > eps)) || ((m == 0) && (A.array().abs() > eps).any())) {
    for (int i = 0; i <= m; i++) {
      sigma(i) = 1.0 / sigma(i);
    }
    (*invA) = svd_A.matrixU() * sigma.asDiagonal() * svd_A.matrixV().transpose();
    return true;
  } else {
    lambda2 = (1 - (sigma(m) / eps) * (sigma(m) / eps)) * lambda_max * lambda_max;
    for (int i = 0; i <= m; i++) {
      if (sigma(i) > EPSQ)
        r++;
      sigma(i) = (sigma(i) / (sigma(i) * sigma(i) + lambda2));
    }
    //only U till the rank
    MatrixD subU = svd_A.matrixU().block(0, 0, A.cols(), r);
    MatrixD subV = svd_A.matrixV().block(0, 0, A.rows(), r);

    (*invA) = subU * sigma.asDiagonal() * subV.transpose();
    return false;
  }

}
コード例 #2
0
bool pinv(const MatrixD &A, MatrixD *invA, Scalar eps) {

  //A (m x n) usually comes from a redundant task jacobian, therfore we consider m<n
  int m = A.rows() - 1;
  VectorD sigma;  //vector of singular values

  JacobiSVD<MatrixD> svd_A(A.transpose(), ComputeThinU | ComputeThinV);
  sigma = svd_A.singularValues();
  if (((m > 0) && (sigma(m) > eps)) || ((m == 0) && (A.array().abs() > eps).any())) {
    for (int i = 0; i <= m; i++) {
      sigma(i) = 1.0 / sigma(i);
    }
    (*invA) = svd_A.matrixU() * sigma.asDiagonal() * svd_A.matrixV().transpose();
    return true;
  } else {
    return false;
  }
}