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#include <catch2/catch_all.hpp>
#include <random>
#include <Eigen/Dense>
#include "sopt/proximal.h"
#include "sopt/sdmm.h"
#include "sopt/types.h"
sopt::t_int random_integer(sopt::t_int min, sopt::t_int max) {
extern std::unique_ptr<std::mt19937_64> mersenne;
std::uniform_int_distribution<sopt::t_int> uniform_dist(min, max);
return uniform_dist(*mersenne);
};
using Scalar = sopt::t_real;
using t_Vector = sopt::Vector<Scalar>;
using t_Matrix = sopt::Matrix<Scalar>;
auto constexpr N = 4;
// Makes members public so we can test one at a time
class IntrospectSDMM : public sopt::algorithm::SDMM<Scalar> {
public:
using sopt::algorithm::SDMM<Scalar>::initialization;
using sopt::algorithm::SDMM<Scalar>::solve_for_xn;
using sopt::algorithm::SDMM<Scalar>::update_directions;
using sopt::algorithm::SDMM<Scalar>::t_Vectors;
using sopt::algorithm::SDMM<Scalar>::t_Vector;
};
TEST_CASE("Proximal translation", "[proximal]") {
using namespace sopt;
t_Vector const translation = t_Vector::Ones(N) * 5;
auto const g = proximal::EuclidianNorm();
auto const gT = proximal::translate(g, -translation);
t_Vector const input = t_Vector::Random(N).array() + 1e0;
CHECK(g(0.1, input).isApprox((1e0 - 0.1 / input.stableNorm()) * input));
auto const gamma = input.stableNorm() * 0.5;
CHECK(g(gamma, input).isApprox((1e0 - gamma / input.stableNorm()) * input));
CHECK(g(gamma * 2 + 1, input).isApprox(input.Zero(N)));
CHECK(gT(0.1, input)
.isApprox((1e0 - 0.1 / (input - translation).stableNorm()) * (input - translation) +
translation));
}
// Iterate through algorithm for special case where the L_i are identies and the objective functions
// are simple euclidian norms
TEST_CASE("Introspect SDMM with L_i = Identity and Euclidian objectives", "[sdmm]") {
using namespace sopt;
t_Matrix const Id = t_Matrix::Identity(N, N).eval();
t_Vector const target0 = t_Vector::Zero(N);
t_Vector const target1 = t_Vector::Random(N);
auto const g0 = proximal::translate(proximal::EuclidianNorm(), -target0);
auto const g1 = proximal::translate(proximal::EuclidianNorm(), -target1);
t_Vector const input = 10 * t_Vector::Random(N);
IntrospectSDMM sdmm = IntrospectSDMM();
sdmm.itermax(10)
.gamma(0.01)
.conjugate_gradient(std::numeric_limits<t_uint>::max(), 1e-12)
.append(g0, Id)
.append(g1, Id);
SECTION("Step by Step") {
INFO("Initialization");
t_Vector out = input;
IntrospectSDMM::t_Vectors y(sdmm.transforms().size(), t_Vector::Zero(out.size()));
IntrospectSDMM::t_Vectors z(sdmm.transforms().size(), t_Vector::Zero(out.size()));
sdmm.initialization(y, z, out);
CHECK(y[0].isApprox(input));
CHECK(y[1].isApprox(input));
INFO("\nThen solve for conjugate gradient");
auto const diagnostic0 = sdmm.solve_for_xn(out, y, z);
CHECK(diagnostic0.good);
CAPTURE(out.transpose());
CAPTURE(input.transpose());
CAPTURE(0.5 * (y[0] + y[1]).transpose());
CHECK(out.isApprox(0.5 * (y[0] + y[1]), 1e-8));
CHECK(out.isApprox(input, 1e-8));
INFO("\nWe move on to first iteration!");
INFO("- updates y and z");
sdmm.update_directions(y, z, out);
CHECK(y[0].isApprox(g0(sdmm.gamma(), input)));
CHECK(y[1].isApprox(g1(sdmm.gamma(), input)));
CHECK(z[0].isApprox(input - y[0]));
CHECK(z[1].isApprox(input - y[1]));
INFO("- solve for conjugate gradient");
auto const diagnostic1 = sdmm.solve_for_xn(out, y, z);
CHECK(diagnostic1.good);
CAPTURE(out.transpose());
CAPTURE((0.5 * (y[0] - z[0] + y[1] - z[1])).transpose());
CHECK(out.isApprox(0.5 * (y[0] - z[0] + y[1] - z[1])));
t_Vector const x1 = g0(sdmm.gamma(), input) + g1(sdmm.gamma(), input) - input;
CHECK(out.isApprox(x1));
INFO("\nWe move on to second iteration!");
INFO("- updates y and z");
sdmm.update_directions(y, z, out);
CHECK(y[0].isApprox(g0(sdmm.gamma(), g1(sdmm.gamma(), input))));
CHECK(y[1].isApprox(g1(sdmm.gamma(), g0(sdmm.gamma(), input))));
CHECK(z[0].isApprox(g1(sdmm.gamma(), input) - y[0]));
CHECK(z[1].isApprox(g0(sdmm.gamma(), input) - y[1]));
INFO("- solve for conjugate gradient");
auto const diagnostic2 = sdmm.solve_for_xn(out, y, z);
CHECK(diagnostic2.good);
CHECK(out.isApprox(0.5 * (y[0] - z[0] + y[1] - z[1])));
t_Vector const x2 = g0(sdmm.gamma(), g1(sdmm.gamma(), input)) +
g1(sdmm.gamma(), g0(sdmm.gamma(), input)) - 0.5 * g1(sdmm.gamma(), input) -
0.5 * g0(sdmm.gamma(), input);
CHECK(out.isApprox(x2));
}
SECTION("Iteration by Iteration") {
t_Vector out;
SECTION("First Iteration") {
sdmm.itermax(1);
auto const diagnostic = sdmm(out, input);
CHECK(not diagnostic.good);
CHECK(diagnostic.niters == 1);
CHECK(out.isApprox(g0(sdmm.gamma(), input) + g1(sdmm.gamma(), input) - input));
}
SECTION("Second Iteration") {
sdmm.itermax(2);
auto const diagnostic = sdmm(out, input);
CHECK(not diagnostic.good);
CHECK(diagnostic.niters == 2);
t_Vector const x2 = g0(sdmm.gamma(), g1(sdmm.gamma(), input)) +
g1(sdmm.gamma(), g0(sdmm.gamma(), input)) -
0.5 * g1(sdmm.gamma(), input) - 0.5 * g0(sdmm.gamma(), input);
CHECK(out.isApprox(x2));
}
SECTION("Nth Iterations") {
sdmm.gamma(1);
for (t_uint itermax(0); itermax < 10; ++itermax) {
t_Vector x = input;
t_Vector y[2] = {x, x};
t_Vector z[2] = {t_Vector::Zero(N).eval(), t_Vector::Zero(N).eval()};
for (t_uint i(0); i < itermax; ++i) {
y[0] = g0(sdmm.gamma(), x + z[0]);
y[1] = g1(sdmm.gamma(), x + z[1]);
z[0] += x - g0(sdmm.gamma(), x + z[0]);
z[1] += x - g1(sdmm.gamma(), x + z[1]);
x = 0.5 * (y[0] - z[0] + y[1] - z[1]);
}
sdmm.itermax(itermax);
auto const diagnostic = sdmm(out, input);
CHECK(out.isApprox(x, 1e-8));
CHECK(not diagnostic.good);
CHECK(diagnostic.niters == itermax);
}
}
}
}
TEST_CASE("SDMM with ||x - x0||_2 functions", "[sdmm][integration]") {
using namespace sopt;
t_Matrix const Id = t_Matrix::Identity(N, N).eval();
t_Vector const target0 = t_Vector::Random(N);
t_Vector target1 = t_Vector::Random(N) * 4;
// for(t_uint i(0); i < N; ++i) target1(i) = i + 1;
auto sdmm = algorithm::SDMM<Scalar>()
.itermax(5000)
.gamma(1)
.conjugate_gradient(std::numeric_limits<t_uint>::max(), 1e-12)
.append(proximal::translate(proximal::EuclidianNorm(), -target0), Id)
.append(proximal::translate(proximal::EuclidianNorm(), -target1), Id);
t_Vector result;
SECTION("Just two operators") {
auto const diagnostic = sdmm(result, t_Vector::Random(N));
CHECK(not diagnostic.good);
CHECK(diagnostic.niters == sdmm.itermax());
t_Vector const segment = (target1 - target0).normalized();
t_real const alpha = (result - target0).transpose() * segment;
CAPTURE(target0.transpose());
CAPTURE(target1.transpose());
CHECK((target1 - target0).transpose() * segment >= alpha);
CHECK(alpha >= 0e0);
CHECK((result - target0 - alpha * segment).stableNorm() < 1e-8);
}
SECTION("Three operators") {
t_Vector const target2 = t_Vector::Random(N) * 8;
sdmm.append(proximal::translate(proximal::EuclidianNorm(), -target2), Id);
auto const diagnostic = sdmm(result, t_Vector::Random(N));
CHECK(not diagnostic.good);
CHECK(diagnostic.niters == sdmm.itermax());
CAPTURE(result.transpose());
auto const func = [&target0, &target1, &target2](t_Vector const &x) {
return (x - target0).stableNorm() + (x - target1).stableNorm() + (x - target2).stableNorm();
};
for (int i(0); i < N; ++i) {
t_Vector epsilon = t_Vector::Zero(N);
epsilon(i) = 1e-6;
CHECK(func(result) < func(result + epsilon));
CHECK(func(result) < func(result - epsilon));
}
}
SECTION("With different L") {
t_Matrix const L0 = t_Matrix::Random(N, N) * 2;
t_Matrix const L1 = t_Matrix::Random(N, N) * 4;
REQUIRE(std::abs((L0.transpose() * L0 + L1.transpose() * L1).determinant()) > 1e-8);
sdmm.itermax(300);
sdmm.transforms(0) = linear_transform(L0);
sdmm.transforms(1) = linear_transform(L1);
auto const diagnostic = sdmm(result, t_Vector::Random(N));
CHECK(not diagnostic.good);
CHECK(diagnostic.niters == sdmm.itermax());
CAPTURE(result.transpose());
auto const func = [&target0, &target1, &L0, &L1](t_Vector const &x) {
return (L0 * x - target0).stableNorm() + (L1 * x - target1).stableNorm();
};
for (int i(0); i < N; ++i) {
t_Vector epsilon = t_Vector::Zero(N);
epsilon(i) = 1e-3;
CAPTURE(epsilon.transpose());
CHECK(func(result) <= func(result + epsilon));
CHECK(func(result) <= func(result - epsilon));
}
}
}
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