problem_test.cc

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#include <gtest/gtest.h>
#include <ifopt/problem.h>
#include <ifopt/ex_problem.h>


using namespace ifopt;

TEST(Problem, GetNumberOfOptimizationVariables)
{
  Problem nlp;
  nlp.AddVariableSet(std::make_shared<ExVariables>("var_set0"));
  nlp.AddVariableSet(std::make_shared<ExVariables>("var_set1"));

  EXPECT_EQ(2+2, nlp.GetNumberOfOptimizationVariables());
}


TEST(Problem, GetBoundsOnOptimizationVariables)
{
  Problem nlp;
  nlp.AddVariableSet(std::make_shared<ExVariables>("var_set0"));
  nlp.AddVariableSet(std::make_shared<ExVariables>("var_set1"));

  auto bounds = nlp.GetBoundsOnOptimizationVariables();
  EXPECT_EQ(2+2, bounds.size());

  // var_set0
  EXPECT_DOUBLE_EQ(-1.0, bounds.at(0).lower_);
  EXPECT_DOUBLE_EQ(+1.0, bounds.at(0).upper_);
  EXPECT_DOUBLE_EQ(-inf, bounds.at(1).lower_);
  EXPECT_DOUBLE_EQ(+inf, bounds.at(1).upper_);

  // var_set1
  EXPECT_DOUBLE_EQ(-1.0, bounds.at(2).lower_);
  EXPECT_DOUBLE_EQ(+1.0, bounds.at(2).upper_);
  EXPECT_DOUBLE_EQ(-inf, bounds.at(3).lower_);
  EXPECT_DOUBLE_EQ(+inf, bounds.at(3).upper_);
}


TEST(Problem, GetVariableValues)
{
  auto var_set0 = std::make_shared<ExVariables>("var_set0");
  var_set0->SetVariables(Eigen::Vector2d(0.1, 0.2));

  auto var_set1 = std::make_shared<ExVariables>("var_set1");
  var_set1->SetVariables(Eigen::Vector2d(0.3, 0.4));

  Problem nlp;
  nlp.AddVariableSet(var_set0);
  nlp.AddVariableSet(var_set1);

  Eigen::VectorXd x = nlp.GetVariableValues();
  EXPECT_EQ(0.1, x(0));
  EXPECT_EQ(0.2, x(1));
  EXPECT_EQ(0.3, x(2));
  EXPECT_EQ(0.4, x(3));
}


TEST(Problem, GetNumberOfConstraints)
{
  Problem nlp;
  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint1"));

  // add same constraints again for testing.
  // notice how the Jacobian calculation inside ExConstraint-class remains the
  //same - the full Jacobian is stitched together accordingly.
  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint2"));

  EXPECT_EQ(1+1, nlp.GetNumberOfConstraints());
}


TEST(Problem, GetBoundsOnConstraints)
{
  Problem nlp;
  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint1"));
  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint2"));

  auto bounds = nlp.GetBoundsOnConstraints();
  // since it's an equality contraint, upper and lower bound are equal
  EXPECT_DOUBLE_EQ(1.0, bounds.at(0).lower_);
  EXPECT_DOUBLE_EQ(1.0, bounds.at(0).upper_);
  EXPECT_DOUBLE_EQ(1.0, bounds.at(1).lower_);
  EXPECT_DOUBLE_EQ(1.0, bounds.at(1).upper_);
}


TEST(Problem, EvaluateConstraints)
{
  Problem nlp;
  nlp.AddVariableSet(std::make_shared<ExVariables>());
  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint1"));
  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint2"));

  double x[2] = { 2.0, 3.0 };
  Eigen::VectorXd g = nlp.EvaluateConstraints(x);
  EXPECT_DOUBLE_EQ(2*2.0+3.0, g(0)); // constant -1 moved to bounds
  EXPECT_DOUBLE_EQ(2*2.0+3.0, g(1)); // constant -1 moved to bounds
}


TEST(Problem, GetJacobianOfConstraints)
{
  Problem nlp;
  nlp.AddVariableSet(std::make_shared<ExVariables>());
  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint1"));
  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint2"));

  double x[2] = { 2.0, 3.0 };
  nlp.SetVariables(x);
  auto jac = nlp.GetJacobianOfConstraints();
  EXPECT_EQ(nlp.GetNumberOfConstraints(), jac.rows());
  EXPECT_EQ(nlp.GetNumberOfOptimizationVariables(), jac.cols());

  EXPECT_DOUBLE_EQ(2*x[0], jac.coeffRef(0,0)); // constraint 1 w.r.t x0
  EXPECT_DOUBLE_EQ(1.0,    jac.coeffRef(0,1)); // constraint 1 w.r.t x1
  EXPECT_DOUBLE_EQ(2*x[0], jac.coeffRef(1,0)); // constraint 2 w.r.t x0
  EXPECT_DOUBLE_EQ(1.0,    jac.coeffRef(1,1)); // constraint 2 w.r.t x1
}


TEST(Problem, EvaluateCostFunction)
{
  Problem nlp;
  nlp.AddVariableSet(std::make_shared<ExVariables>());
  nlp.AddCostSet(std::make_shared<ExCost>("cost_term1"));
  nlp.AddCostSet(std::make_shared<ExCost>("cost_term2"));

  EXPECT_TRUE(nlp.HasCostTerms());

  double x[2] = { 2.0, 3.0 };
  EXPECT_DOUBLE_EQ(2*(-std::pow(x[1]-2.0,2)), nlp.EvaluateCostFunction(x)); // constant -1 moved to bounds
}


TEST(Problem, HasCostTerms)
{
  Problem nlp;
  EXPECT_FALSE(nlp.HasCostTerms());

  nlp.AddVariableSet(std::make_shared<ExVariables>());
  EXPECT_FALSE(nlp.HasCostTerms());

  nlp.AddConstraintSet(std::make_shared<ExConstraint>());
  EXPECT_FALSE(nlp.HasCostTerms());

  nlp.AddCostSet(std::make_shared<ExCost>());
  EXPECT_TRUE(nlp.HasCostTerms());
}


TEST(Problem, EvaluateCostFunctionGradient)
{
  Problem nlp;
  nlp.AddVariableSet(std::make_shared<ExVariables>());
  nlp.AddCostSet(std::make_shared<ExCost>("cost_term1"));
  nlp.AddCostSet(std::make_shared<ExCost>("cost_term2"));

  double x[2] = { 2.0, 3.0 };
  Eigen::VectorXd grad = nlp.EvaluateCostFunctionGradient(x);

  EXPECT_EQ(nlp.GetNumberOfOptimizationVariables(), grad.rows());
  EXPECT_DOUBLE_EQ(0.0,             grad(0)); // cost1+cost2 w.r.t x0
  EXPECT_DOUBLE_EQ(2*(-2*(x[1]-2)), grad(1)); // cost1+cost2 w.r.t x1
}