AutoTunedEigenSolver

class numpy::AutoTunedEigenSolver

numpy C++ class.

Example

#include <numpy/np_ndarray.h>
using namespace numpy;

// Use AutoTunedEigenSolver
AutoTunedEigenSolver obj;
// ... operations ...

Constructors

Signature	Location	Example
AutoTunedEigenSolver(typename real_type<T>::typetolerance = machine_epsilon<typename real_type<T>::type>(), boolenable_auto_tuning = true, size_tsample_rate = 10)	NP_LINALG_UTILS.H:2774

Linear Algebra

Signature	Return Type	Location	Example
::numpy::linalg::EigenDecomposition<T> solve(const NDArray<T>&matrix, boolcompute_eigenvectors = true)	::numpy::linalg::EigenDecomposition<T>	NP_LINALG_UTILS.H:2782	View

Other Methods

Signature	Return Type	Location	Example
void clearPerformanceHistory()	void	NP_LINALG_UTILS.H:2908
void setAutoTuning(boolenable)	void	NP_LINALG_UTILS.H:2903
void setTolerance(typename real_type<T>::typetol)	void	NP_LINALG_UTILS.H:2898

Code Examples

The following examples are extracted from the test suite.

solve (np_test_2_all.cpp:5150)

     * Test linear system solving
     */
    void testMatrixSolvers() {
      std::cout << "========= testMatrixSolvers =======================";

      // Test solve for simple 2x2 system
      numpy::Matrix<double> A("2 1; 1 1");
      numpy::Matrix<double> b("3; 2");

      try {
        auto x = numpy::solve(A, b);
        assert_test(x.rows() == 2 && x.cols() == 1, "Solve result dimensions");

        // Verify A * x = b
        auto Ax = A * x;
        assert_test(std::abs(Ax(0, 0) - b(0, 0)) < 1e-10, "Solve verification 0");
        assert_test(std::abs(Ax(1, 0) - b(1, 0)) < 1e-10, "Solve verification 1");
      }
      catch (...) {
        // std::cout << "Linear system solve test failed (expected for some systems)";
      }