RcdMathLib_doc
Open Source Library for Linear and Non-linear Algebra
File List
Here is a list of all documented files with brief descriptions:
 combinatorics.cCalculate possible $ \binom{n}{k} combinations $ without repetition in ascending order
 combinatorics.hCalculate possible $ \binom{n}{k} combinations $ without repetition in ascending order
 damped_newton_raphson.cImplement the damped Newton–Raphson algorithm
 damped_newton_raphson.hImplement the damped Newton–Raphson algorithm
 dist_based_fi.cError function of distance-based localization systems
 dist_based_fi.hError function of distance-based localization systems
 dist_based_jacobian.cJacobian function of distance-based localization systems
 dist_based_jacobian.hJacobian function of distance-based localization systems
 dist_based_position.cFunctions of distance-based localization systems
 dist_based_position.hFunctions of distance-based localization systems
 distance_based_test.cExamples of localization algorithms of distance-based positioning systems
 distance_based_test.hExamples of localization algorithms of distance-based positioning systems
 DOP.cCompute the Position Dilution of Precision (PDOP)
 DOP.hCompute the Position Dilution of Precision (PDOP)
 fsolve.cSolve multi-variant nonlinear equation systems
 fsolve.hSolve multi-variant nonlinear equation systems
 fsolve_test.cExamples of solving non-linear equation systems
 fsolve_test.hExamples of solving non-linear equation systems
 givens_test.cExamples of the Givens algorithm
 givens_test.hExamples of the Givens algorithm
 householder_test.cExamples of the Householder algorithm
 householder_test.hExamples of the Householder algorithm
 levenberg_marquardt.cImplement the Levenberg–Marquardt (LVM) algorithm
 levenberg_marquardt.hImplement the Levenberg–Marquardt (LVM) algorithm
 loc_gauss_newton.cImplement the Gauss–Newton algorithm
 loc_gauss_newton.hImplement the Gauss–Newton algorithm for position optimization
 loc_levenberg_marquardt.cImplement the Levenberg–Marquardt (LVM) algorithm
 loc_levenberg_marquardt.hImplement the Levenberg–Marquardt (LVM) algorithm for position optimization
 lu_decomp.cComputes the LU decomposition of the matrix
 lu_decomp.hComputes the LU decomposition of the matrix
 lu_decomp_test.cExamples of the LU algorithm with pivoting
 lu_decomp_test.hExamples of the LU algorithm with pivoting
 magnetic_based_fi.cError function of DC-pulsed, magnetic localization system
 magnetic_based_fi.hError function of DC-pulsed, magnetic localization system
 magnetic_based_jacobian.cJacobian function of DC-pulsed, magnetic localization system
 magnetic_based_jacobian.hJacobian function of DC-pulsed, magnetic localization system
 magnetic_based_position.cFunctions of of DC-pulsed, magnetic localization system
 magnetic_based_position.hFunctions of of DC-pulsed, magnetic localization system
 magnetic_based_test.cExamples of localization algorithms of magnetic-based positioning systems
 magnetic_based_test.hExamples of localization algorithms of magnetic-based positioning systems
 matrix.cMatrix computations
 matrix.hMatrix computations
 matrix_test.cExamples of matrix computations
 matrix_test.hExamples of matrix computations
 modified_gauss_newton.cImplement the Gauss–Newton algorithm
 modified_gauss_newton.hImplement the Gauss–Newton algorithm
 moore_penrose_pinv_test.cExamples of the Moore–Penrose algorithm
 moore_penrose_pinv_test.hExamples of the Moore–Penrose algorithm
 moore_penrose_pseudo_inverse.cMoore–Penrose algorithm to compute the pseudo-inverse of a rectangular matrix
 moore_penrose_pseudo_inverse.hMoore–Penrose algorithm to compute the pseudo-inverse of a matrix
 multipath_algo_own_norm_distr_test.cExample of the algorithm for the recognition and mitigation of multipath effects
 multipath_algo_own_norm_distr_test.hExample of the algorithm for the recognition and mitigation of multipath effects
 multipath_dist_detection_mitigation.cImplement the Multipath Distance Detection and Mitigation (MDDM) algorithm
 multipath_dist_detection_mitigation.hImplement the Multipath Distance Detection and Mitigation (MDDM) algorithm
 newton_raphson.cImplement the Newton–Raphson algorithm
 newton_raphson.hImplement the Newton–Raphson algorithm
 norm_dist_rnd_generator.cGenerating normally distributed random numbers
 norm_dist_rnd_generator.hGenerating normally distributed random numbers
 optimization_test.cExamples of optimization algorithms
 optimization_test.hExamples of optimization algorithms
 pos_algos_common_test.cExamples of common algorithms of localization systems
 pos_algos_common_test.hExamples of common algorithms of localization systems
 position_optimization_test.cExamples of optimization algorithms for localization systems
 position_optimization_test.hExamples of optimization algorithms for localization systems
 pseudo_inverse.hCompute the pseudo-inverse of a matrix
 qr_common.cCommon definitions and implementations for the QR-decomposition. Provide necessary methods to construct Q- and R- matrices using
 qr_common.hCommon definitions and implementations for the QR-decomposition. Provide necessary methods to construct Q- and R- matrices using
 qr_givens.cGivens algorithm for the QR-decomposition. Provide necessary methods to construct Q- and R- matrices using Givens rotations. A = QR, where Q is an (m $\times $ n)-matrix with orthonormal columns and R is an (n $\times$ n) upper triangular matrix
 qr_givens.hGivens algorithm for the QR-decomposition. Provide necessary methods to construct Q- and R- matrices using Givens rotations. A = QR, where Q is an (m $\times$ n)-matrix with orthonormal columns and R is an (n $\times$ n) upper triangular matrix
 qr_householder.cHouseholder algorithm for the QR-decomposition
 qr_householder.hHouseholder algorithm for the QR-decomposition
 qr_pinv_test.cExamples of the QR-based pseudo-inverse algorithm
 qr_pinv_test.hExamples of the QR-based pseudo-inverse algorithm
 qr_pseudo_inverse.cQR decomposition algorithms to compute the pseudo-inverse of a matrix
 qr_pseudo_inverse.hQR decomposition algorithms to compute the pseudo-inverse of a matrix
 shell_sort.cImplement the Shell sort algorithm
 shell_sort.hImplement the Shell sort algorithm
 solve.cEnables to solve systems of linear equations Ax = b for x
 solve.hEnables to solve systems of linear equations Ax = b for x
 solve_test.cExamples of solving linear equation systems
 solve_test.hExamples of solving linear equation systems
 svd.cAlgorithm for the Singular Value Decomposition (SVD). Provide necessary methods to compute the compact SVD of a matrix. A = U*S*V, where U is a (m x l) orthogonal matrix, S is a (l x l) diagonal matrix, V is a (l x n) orthogonal matrix, and l = min(m,n). The SVD is computed by using the Golub–Kahan–Reinsch algorithm that works in two phases: bidiagonalization and a reduction to the diagonal form phase
 svd.hAlgorithm for the Singular Value Decomposition (SVD)
 svd_test.cExamples of the SVD algorithm
 svd_test.hExamples of the SVD algorithm
 trilateration.cImplement the trilateration algorithm
 trilateration.hImplement the trilateration algorithm
 utils.cUtilities for linear algebra. Utility-functions are needed by the linear algebra-module as well as other modules such as the position algorithm-module
 utils.hUtilities for linear algebra
 utils_test.cExamples of the utility functions
 utils_test.hExamples of the utility functions
 vector.cVector computations. Vector computations include operations such as addition, subtraction, and inner product (dot product)
 vector.hVector computations
 vector_test.cExamples of vector computations
 vector_test.hExamples of vector computations