linear_transform.cc File Reference

#include <catch2/catch_all.hpp>
#include <complex>
#include <iomanip>
#include "sopt/linear_transform.h"

Include dependency graph for linear_transform.cc:

Go to the source code of this file.

Functions
	TEST_CASE ("Linear Transforms", "[ops]")
	TEST_CASE ("Array of Linear transforms", "[ops]")

Function Documentation

TEST_CASE() [1/2]

TEST_CASE	(	"Array of Linear transforms"	,
		""	[ops]
	)

Definition at line 60 of file linear_transform.cc.

                                                 {
  using namespace sopt;
 
  using SCALAR = int;
  using t_Vector = Vector<SCALAR>;
  using t_Matrix = Matrix<SCALAR>;
 
  auto constexpr N = 10;
  t_Vector const x = t_Vector::Random(N) * 5;
  std::vector<t_Matrix> Ls{t_Matrix::Random(N, N), t_Matrix::Random(N, N)};
  std::vector<LinearTransform<t_Vector>> ops;
  ops.reserve(Ls.size());
  for (auto const &matrix : Ls) ops.emplace_back(sopt::linear_transform(matrix));
 
  for (decltype(Ls)::size_type i(0); i < ops.size(); ++i) {
    CHECK(ops[i] * x == Ls[i] * x);
    CHECK(ops[i].adjoint() * x == Ls[i].conjugate().transpose() * x);
  }
}

References sopt::linear_transform(), and N.

TEST_CASE() [2/2]

TEST_CASE	(	"Linear Transforms"	,
		""	[ops]
	)

Definition at line 7 of file linear_transform.cc.

                                        {
  using namespace sopt;
 
  using SCALAR = int;
  using t_Vector = Array<SCALAR>;
  using t_Matrix = Image<SCALAR>;
  auto constexpr N = 10;
 
  SECTION("Functions") {
    auto direct = [](t_Vector &out, t_Vector const &input) { out = input * 2 - 1; };
    auto indirect = [](t_Vector &out, t_Vector const &input) { out = input * 4 - 1; };
    t_Vector const x = t_Vector::Random(2 * N) * 5;
 
    auto op = sopt::linear_transform<t_Vector>(direct, indirect);
 
    CHECK((op * x).eval().cols() == x.cols());
    CHECK((op * x).eval().rows() == x.rows());
    CHECK((op * x).cols() == x.cols());
    CHECK((op * x).rows() == x.rows());
    CHECK((op * x).matrix() == (2 * x - 1).matrix());
    CHECK((op.adjoint() * x).matrix() == (4 * x - 1).matrix());
  }
 
  SECTION("Matrix") {
    t_Matrix const L = t_Matrix::Random(N, N);
    t_Vector const x = t_Vector::Random(N) * 5;
 
    auto op = sopt::linear_transform(L.matrix());
 
    CHECK((op * x.matrix()).eval().cols() == x.cols());
    CHECK((op * x.matrix()).eval().rows() == x.rows());
    CHECK((op * x.matrix()).cols() == x.cols());
    CHECK((op * x.matrix()).rows() == x.rows());
    CHECK(op * x.matrix() == L.matrix() * x.matrix());
    CHECK(op.adjoint() * x.matrix() == L.conjugate().transpose().matrix() * x.matrix());
  }
 
  SECTION("Rectangular matrix") {
    t_Matrix const L = t_Matrix::Random(N, 2 * N);
    t_Vector const x = t_Vector::Random(2 * N) * 5;
 
    auto op = sopt::linear_transform(L.matrix());
 
    CHECK((op * x.matrix()).eval().cols() == 1);
    CHECK((op * x.matrix()).eval().rows() == N);
    CHECK((op * x.matrix()).cols() == 1);
    CHECK((op * x.matrix()).rows() == N);
    CHECK(op * x.matrix() == L.matrix() * x.matrix());
    CHECK(op.adjoint() * x.head(N).matrix() ==
          L.conjugate().transpose().matrix() * x.head(N).matrix());
  }
}

References cols, sopt::linear_transform(), N, and rows.