problem_test.cc
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29 
30 #include <gtest/gtest.h>
31 
32 #include <ifopt/problem.h>
33 #include <ifopt/test_vars_constr_cost.h>
34 
35 using namespace ifopt;
36 
37 TEST(Problem, GetNumberOfOptimizationVariables)
38 {
39  Problem nlp;
40  nlp.AddVariableSet(std::make_shared<ExVariables>("var_set0"));
41  nlp.AddVariableSet(std::make_shared<ExVariables>("var_set1"));
42 
43  EXPECT_EQ(2 + 2, nlp.GetNumberOfOptimizationVariables());
44 }
45 
46 TEST(Problem, GetBoundsOnOptimizationVariables)
47 {
48  Problem nlp;
49  nlp.AddVariableSet(std::make_shared<ExVariables>("var_set0"));
50  nlp.AddVariableSet(std::make_shared<ExVariables>("var_set1"));
51 
52  auto bounds = nlp.GetBoundsOnOptimizationVariables();
53  EXPECT_EQ(2 + 2, bounds.size());
54 
55  // var_set0
56  EXPECT_DOUBLE_EQ(-1.0, bounds.at(0).lower_);
57  EXPECT_DOUBLE_EQ(+1.0, bounds.at(0).upper_);
58  EXPECT_DOUBLE_EQ(-inf, bounds.at(1).lower_);
59  EXPECT_DOUBLE_EQ(+inf, bounds.at(1).upper_);
60 
61  // var_set1
62  EXPECT_DOUBLE_EQ(-1.0, bounds.at(2).lower_);
63  EXPECT_DOUBLE_EQ(+1.0, bounds.at(2).upper_);
64  EXPECT_DOUBLE_EQ(-inf, bounds.at(3).lower_);
65  EXPECT_DOUBLE_EQ(+inf, bounds.at(3).upper_);
66 }
67 
68 TEST(Problem, GetVariableValues)
69 {
70  auto var_set0 = std::make_shared<ExVariables>("var_set0");
71  var_set0->SetVariables(Eigen::Vector2d(0.1, 0.2));
72 
73  auto var_set1 = std::make_shared<ExVariables>("var_set1");
74  var_set1->SetVariables(Eigen::Vector2d(0.3, 0.4));
75 
76  Problem nlp;
77  nlp.AddVariableSet(var_set0);
78  nlp.AddVariableSet(var_set1);
79 
80  Eigen::VectorXd x = nlp.GetVariableValues();
81  EXPECT_EQ(0.1, x(0));
82  EXPECT_EQ(0.2, x(1));
83  EXPECT_EQ(0.3, x(2));
84  EXPECT_EQ(0.4, x(3));
85 }
86 
87 TEST(Problem, GetNumberOfConstraints)
88 {
89  Problem nlp;
90  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint1"));
91 
92  // add same constraints again for testing.
93  // notice how the Jacobian calculation inside ExConstraint-class remains the
94  //same - the full Jacobian is stitched together accordingly.
95  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint2"));
96 
97  EXPECT_EQ(1 + 1, nlp.GetNumberOfConstraints());
98 }
99 
100 TEST(Problem, GetBoundsOnConstraints)
101 {
102  Problem nlp;
103  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint1"));
104  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint2"));
105 
106  auto bounds = nlp.GetBoundsOnConstraints();
107  // since it's an equality contraint, upper and lower bound are equal
108  EXPECT_DOUBLE_EQ(1.0, bounds.at(0).lower_);
109  EXPECT_DOUBLE_EQ(1.0, bounds.at(0).upper_);
110  EXPECT_DOUBLE_EQ(1.0, bounds.at(1).lower_);
111  EXPECT_DOUBLE_EQ(1.0, bounds.at(1).upper_);
112 }
113 
114 TEST(Problem, EvaluateConstraints)
115 {
116  Problem nlp;
117  nlp.AddVariableSet(std::make_shared<ExVariables>());
118  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint1"));
119  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint2"));
120 
121  double x[2] = {2.0, 3.0};
122  Eigen::VectorXd g = nlp.EvaluateConstraints(x);
123  EXPECT_DOUBLE_EQ(2 * 2.0 + 3.0, g(0)); // constant -1 moved to bounds
124  EXPECT_DOUBLE_EQ(2 * 2.0 + 3.0, g(1)); // constant -1 moved to bounds
125 }
126 
127 TEST(Problem, GetJacobianOfConstraints)
128 {
129  Problem nlp;
130  nlp.AddVariableSet(std::make_shared<ExVariables>());
131  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint1"));
132  nlp.AddConstraintSet(std::make_shared<ExConstraint>("constraint2"));
133 
134  double x[2] = {2.0, 3.0};
135  nlp.SetVariables(x);
136  auto jac = nlp.GetJacobianOfConstraints();
137  EXPECT_EQ(nlp.GetNumberOfConstraints(), jac.rows());
138  EXPECT_EQ(nlp.GetNumberOfOptimizationVariables(), jac.cols());
139 
140  EXPECT_DOUBLE_EQ(2 * x[0], jac.coeffRef(0, 0)); // constraint 1 w.r.t x0
141  EXPECT_DOUBLE_EQ(1.0, jac.coeffRef(0, 1)); // constraint 1 w.r.t x1
142  EXPECT_DOUBLE_EQ(2 * x[0], jac.coeffRef(1, 0)); // constraint 2 w.r.t x0
143  EXPECT_DOUBLE_EQ(1.0, jac.coeffRef(1, 1)); // constraint 2 w.r.t x1
144 }
145 
146 TEST(Problem, EvaluateCostFunction)
147 {
148  Problem nlp;
149  nlp.AddVariableSet(std::make_shared<ExVariables>());
150  nlp.AddCostSet(std::make_shared<ExCost>("cost_term1"));
151  nlp.AddCostSet(std::make_shared<ExCost>("cost_term2"));
152 
153  EXPECT_TRUE(nlp.HasCostTerms());
154 
155  double x[2] = {2.0, 3.0};
156  EXPECT_DOUBLE_EQ(2 * (-std::pow(x[1] - 2.0, 2)),
157  nlp.EvaluateCostFunction(x)); // constant -1 moved to bounds
158 }
159 
160 TEST(Problem, HasCostTerms)
161 {
162  Problem nlp;
163  EXPECT_FALSE(nlp.HasCostTerms());
164 
165  nlp.AddVariableSet(std::make_shared<ExVariables>());
166  EXPECT_FALSE(nlp.HasCostTerms());
167 
168  nlp.AddConstraintSet(std::make_shared<ExConstraint>());
169  EXPECT_FALSE(nlp.HasCostTerms());
170 
171  nlp.AddCostSet(std::make_shared<ExCost>());
172  EXPECT_TRUE(nlp.HasCostTerms());
173 }
174 
175 TEST(Problem, EvaluateCostFunctionGradient)
176 {
177  Problem nlp;
178  nlp.AddVariableSet(std::make_shared<ExVariables>());
179  nlp.AddCostSet(std::make_shared<ExCost>("cost_term1"));
180  nlp.AddCostSet(std::make_shared<ExCost>("cost_term2"));
181 
182  double x[2] = {2.0, 3.0};
183  Eigen::VectorXd grad = nlp.EvaluateCostFunctionGradient(x);
184 
185  EXPECT_EQ(nlp.GetNumberOfOptimizationVariables(), grad.rows());
186  EXPECT_DOUBLE_EQ(0.0, grad(0)); // cost1+cost2 w.r.t x0
187  EXPECT_DOUBLE_EQ(2 * (-2 * (x[1] - 2)), grad(1)); // cost1+cost2 w.r.t x1
188 }
ifopt::Problem::GetNumberOfOptimizationVariables
int GetNumberOfOptimizationVariables() const
The number of optimization variables.
Definition: problem.cc:86
ifopt::Problem::EvaluateCostFunction
double EvaluateCostFunction(const double *x)
The scalar cost for current optimization variables x.
Definition: problem.cc:106
ifopt::Problem::SetVariables
void SetVariables(const double *x)
Updates the variables with the values of the raw pointer x.
Definition: problem.cc:101
ifopt::Problem::EvaluateCostFunctionGradient
VectorXd EvaluateCostFunctionGradient(const double *x, bool use_finite_difference_approximation=false, double epsilon=std::numeric_limits< double >::epsilon())
The column-vector of derivatives of the cost w.r.t. each variable.
Definition: problem.cc:116
ifopt::Problem::GetBoundsOnConstraints
VecBound GetBoundsOnConstraints() const
The upper and lower bound of each individual constraint.
Definition: problem.cc:143
ifopt::Problem::HasCostTerms
bool HasCostTerms() const
True if the optimization problem includes a cost, false if merely a feasibility problem is defined.
Definition: problem.cc:159
ifopt::Problem::AddConstraintSet
void AddConstraintSet(ConstraintSet::Ptr constraint_set)
Add a set of multiple constraints to the optimization problem.
Definition: problem.cc:74
problem.h
ifopt::Problem::GetNumberOfConstraints
int GetNumberOfConstraints() const
The number of individual constraints.
Definition: problem.cc:148
ifopt::Problem::GetJacobianOfConstraints
Jacobian GetJacobianOfConstraints() const
The sparse-matrix representation of Jacobian of the constraints.
Definition: problem.cc:173
TEST
TEST(Problem, GetNumberOfOptimizationVariables)
Definition: problem_test.cc:37
ifopt::Problem
A generic optimization problem with variables, costs and constraints.
Definition: problem.h:123
ifopt::Problem::EvaluateConstraints
VectorXd EvaluateConstraints(const double *x)
Each constraint value g(x) for current optimization variables x.
Definition: problem.cc:153
ifopt::Problem::GetVariableValues
VectorXd GetVariableValues() const
The current value of the optimization variables.
Definition: problem.cc:96
ifopt::Problem::GetBoundsOnOptimizationVariables
VecBound GetBoundsOnOptimizationVariables() const
The maximum and minimum value each optimization variable is allowed to have.
Definition: problem.cc:91
ifopt::Problem::AddCostSet
void AddCostSet(CostTerm::Ptr cost_set)
Add a cost term to the optimization problem.
Definition: problem.cc:80
test_vars_constr_cost.h
Example to generate a solver-independent formulation for the problem, taken from the IPOPT cpp_exampl...
ifopt::Problem::AddVariableSet
void AddVariableSet(VariableSet::Ptr variable_set)
Add one individual set of variables to the optimization problem.
Definition: problem.cc:69
ifopt
common namespace for all elements in this library.
Definition: bounds.h:33
ifopt::inf
static const double inf
Definition: bounds.h:92

ifopt
Author(s): Alexander W. Winkler
autogenerated on Mon Sep 18 2023 02:14:38