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fix several memory leaks #134

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Merged
merged 8 commits into from
Jun 11, 2025
Merged

fix several memory leaks #134

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c12fa63
fix several memory leaks
superwhiskers Jun 10, 2025
e33a97c
Apply pre-commmit fixes
superwhiskers Jun 10, 2025
4d0a07d
set both backward and forward linear solvers and jacobian matrices to…
superwhiskers Jun 10, 2025
8b9737f
Apply pre-commmit fixes
superwhiskers Jun 10, 2025
cf3d29b
remove explicit nullptr
superwhiskers Jun 10, 2025
29d3221
Avoid repeated allocations.
pelesh Jun 10, 2025
e7d9690
Apply pre-commmit fixes
pelesh Jun 10, 2025
15546ee
Create buses on stack.
pelesh Jun 10, 2025
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66 changes: 32 additions & 34 deletions examples/Experimental/DynamicConstrainedOpt/DynamicConstrainedOpt.cpp
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@

#include <iomanip>
#include <iostream>
#include <memory>

#include <IpIpoptApplication.hpp>
#include <IpSolveStatistics.hpp>
Expand All @@ -20,47 +20,47 @@ int main()
using namespace GridKit::Testing;

// Create an infinite bus
BaseBus<double, size_t>* bus = new BusSlack<double, size_t>(1.0, 0.0);
std::unique_ptr<BaseBus<double, size_t>> bus = std::make_unique<BusSlack<double, size_t>>(1.0, 0.0);

// Attach a generator to that bus
Generator2<double, size_t>* gen = new Generator2<double, size_t>(bus);
Generator2<double, size_t> gen(bus.get());

// Create a system model
SystemModel<double, size_t>* model = new SystemModel<double, size_t>();
model->addBus(bus);
model->addComponent(gen);
SystemModel<double, size_t> model;
model.addBus(bus.get());
model.addComponent(&gen);

// allocate model components
model->allocate();
model.allocate();

// Create numerical integrator and configure it for the generator model
Ida<double, size_t>* idas = new Ida<double, size_t>(model);
Ida<double, size_t> idas(&model);

double t_init = 0.0;
double t_final = 20.0;

// setup simulation
idas->configureSimulation();
idas->configureAdjoint();
idas->getDefaultInitialCondition();
idas->initializeSimulation(t_init);
idas->configureQuadrature();
idas->initializeQuadrature();
idas.configureSimulation();
idas.configureAdjoint();
idas.getDefaultInitialCondition();
idas.initializeSimulation(t_init);
idas.configureQuadrature();
idas.initializeQuadrature();

double t_fault = 0.02;
double t_clear = 0.06;
idas->runSimulation(t_fault);
idas.runSimulation(t_fault);
// create initial condition after a fault
{
gen->V() = 0.0;
idas->runSimulation(t_clear, 2);
gen->V() = 1.0;
gen->theta() = -0.01;
idas->saveInitialCondition();
gen.V() = 0.0;
idas.runSimulation(t_clear, 2);
gen.V() = 1.0;
gen.theta() = -0.01;
idas.saveInitialCondition();
}

// Set integration time for dynamic constrained optimization
idas->setIntegrationTime(t_init, t_final, 100);
idas.setIntegrationTime(t_init, t_final, 100);

// Guess optimization parameter value
double Pm = 0.7;
Expand All @@ -87,10 +87,10 @@ int main()

// Create dynamic objective interface to Ipopt solver
Ipopt::SmartPtr<Ipopt::TNLP> ipoptDynamicObjectiveInterface =
new IpoptInterface::DynamicObjective<double, size_t>(idas);
new IpoptInterface::DynamicObjective<double, size_t>(&idas);

// Initialize problem
model->param()[0] = Pm;
model.param()[0] = Pm;

// Solve the problem
status = ipoptApp->OptimizeTNLP(ipoptDynamicObjectiveInterface);
Expand All @@ -101,24 +101,24 @@ int main()
// Print result
std::cout << "\nSucess:\n The problem solved in "
<< ipoptApp->Statistics()->IterationCount() << " iterations!\n"
<< " Optimal value of Pm = " << model->param()[0] << "\n"
<< " Optimal value of Pm = " << model.param()[0] << "\n"
<< " The final value of the objective function G(Pm) = "
<< ipoptApp->Statistics()->FinalObjective() << "\n\n";
}

// Store dynamic objective optimization results
double* results = new double[model->sizeParams()];
for (unsigned i = 0; i < model->sizeParams(); ++i)
double* results = new double[model.sizeParams()];
for (unsigned i = 0; i < model.sizeParams(); ++i)
{
results[i] = model->param()[i];
results[i] = model.param()[i];
}

// Create dynamic constraint interface to Ipopt solver
Ipopt::SmartPtr<Ipopt::TNLP> ipoptDynamicConstraintInterface =
new IpoptInterface::DynamicConstraint<double, size_t>(idas);
new IpoptInterface::DynamicConstraint<double, size_t>(&idas);

// Initialize problem
model->param()[0] = Pm;
model.param()[0] = Pm;

// Solve the problem
status = ipoptApp->OptimizeTNLP(ipoptDynamicConstraintInterface);
Expand All @@ -129,16 +129,16 @@ int main()
// Print result
std::cout << "\nSucess:\n The problem solved in "
<< ipoptApp->Statistics()->IterationCount() << " iterations!\n"
<< " Optimal value of Pm = " << model->param()[0] << "\n"
<< " Optimal value of Pm = " << model.param()[0] << "\n"
<< " The final value of the objective function G(Pm) = "
<< ipoptApp->Statistics()->FinalObjective() << "\n\n";
}

// Compare results of the two optimization methods
int retval = 0;
for (unsigned i = 0; i < model->sizeParams(); ++i)
for (unsigned i = 0; i < model.sizeParams(); ++i)
{
if (!isEqual(results[i], model->param()[i], 10 * tol))
if (!isEqual(results[i], model.param()[i], 10 * tol))
--retval;
}

Expand All @@ -148,7 +148,5 @@ int main()
}

delete[] results;
delete idas;
delete model;
return retval;
}
78 changes: 38 additions & 40 deletions examples/Experimental/GenInfiniteBus/GenInfiniteBus.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@

#include <iomanip>
#include <iostream>
#include <memory>

#include <IpIpoptApplication.hpp>
#include <IpSolveStatistics.hpp>
Expand All @@ -20,49 +20,49 @@ int main()
using namespace GridKit::Testing;

// Create an infinite bus
BaseBus<double, size_t>* bus = new BusSlack<double, size_t>(1.0, 0.0);
std::unique_ptr<BaseBus<double, size_t>> bus = std::make_unique<BusSlack<double, size_t>>(1.0, 0.0);

// Attach a generator to that bus
Generator4<double, size_t>* gen = new Generator4<double, size_t>(bus);
Generator4<double, size_t> gen(bus.get());

// Create a system model
SystemModel<double, size_t>* model = new SystemModel<double, size_t>();
model->addBus(bus);
model->addComponent(gen);
SystemModel<double, size_t> model;
model.addBus(bus.get());
model.addComponent(&gen);

// allocate model components
model->allocate();
model.allocate();

// Create numerical integrator and configure it for the generator model
Ida<double, size_t>* idas = new Ida<double, size_t>(model);
Ida<double, size_t> idas(&model);

double t_init = 0.0;
double t_final = 15.0;

// setup simulation
idas->configureSimulation();
idas->configureAdjoint();
idas->getDefaultInitialCondition();
idas->initializeSimulation(t_init);
idas->configureQuadrature();
idas->initializeQuadrature();
idas.configureSimulation();
idas.configureAdjoint();
idas.getDefaultInitialCondition();
idas.initializeSimulation(t_init);
idas.configureQuadrature();
idas.initializeQuadrature();

double t_fault = 0.1;
double t_clear = 0.1;
idas->runSimulation(t_fault);
idas->saveInitialCondition();
idas.runSimulation(t_fault);
idas.saveInitialCondition();
// create initial condition after a fault
{
idas->getSavedInitialCondition();
idas->initializeSimulation(t_init);
gen->V() = 0.0;
idas->runSimulation(t_clear, 20);
gen->V() = 1.0;
idas->saveInitialCondition();
idas.getSavedInitialCondition();
idas.initializeSimulation(t_init);
gen.V() = 0.0;
idas.runSimulation(t_clear, 20);
gen.V() = 1.0;
idas.saveInitialCondition();
}

// Set integration time for dynamic constrained optimization
idas->setIntegrationTime(t_init, t_final, 100);
idas.setIntegrationTime(t_init, t_final, 100);

// Guess initial values of optimization parameters
double Pm = 1.0;
Expand Down Expand Up @@ -90,11 +90,11 @@ int main()

// Create dynamic objective interface to Ipopt solver
Ipopt::SmartPtr<Ipopt::TNLP> ipoptDynamicObjectiveInterface =
new IpoptInterface::DynamicObjective<double, size_t>(idas);
new IpoptInterface::DynamicObjective<double, size_t>(&idas);

// Initialize the problem
model->param()[0] = Pm;
model->param()[1] = Ef;
model.param()[0] = Pm;
model.param()[1] = Ef;

// Solve the problem
status = ipoptApp->OptimizeTNLP(ipoptDynamicObjectiveInterface);
Expand All @@ -105,26 +105,26 @@ int main()
// Print result
std::cout << "\nSucess:\n The problem solved in "
<< ipoptApp->Statistics()->IterationCount() << " iterations!\n"
<< " Optimal value of Pm = " << model->param()[0] << "\n"
<< " Optimal value of Ef = " << model->param()[1] << "\n"
<< " Optimal value of Pm = " << model.param()[0] << "\n"
<< " Optimal value of Ef = " << model.param()[1] << "\n"
<< " The final value of the objective function G(Pm,Ef) = "
<< ipoptApp->Statistics()->FinalObjective() << "\n\n";
}

// Create dynamic constraint interface to Ipopt solver
Ipopt::SmartPtr<Ipopt::TNLP> ipoptDynamicConstraintInterface =
new IpoptInterface::DynamicConstraint<double, size_t>(idas);
new IpoptInterface::DynamicConstraint<double, size_t>(&idas);

// Store dynamic objective optimization results
double* results = new double[model->sizeParams()];
for (unsigned i = 0; i < model->sizeParams(); ++i)
double* results = new double[model.sizeParams()];
for (unsigned i = 0; i < model.sizeParams(); ++i)
{
results[i] = model->param()[i];
results[i] = model.param()[i];
}

// Initialize the problem
model->param()[0] = Pm;
model->param()[1] = Ef;
model.param()[0] = Pm;
model.param()[1] = Ef;

// Solve the problem
status = ipoptApp->OptimizeTNLP(ipoptDynamicConstraintInterface);
Expand All @@ -135,17 +135,17 @@ int main()
// Print result
std::cout << "\nSucess:\n The problem solved in "
<< ipoptApp->Statistics()->IterationCount() << " iterations!\n"
<< " Optimal value of Pm = " << model->param()[0] << "\n"
<< " Optimal value of Ef = " << model->param()[1] << "\n"
<< " Optimal value of Pm = " << model.param()[0] << "\n"
<< " Optimal value of Ef = " << model.param()[1] << "\n"
<< " The final value of the objective function G(Pm,Ef) = "
<< ipoptApp->Statistics()->FinalObjective() << "\n\n";
}

// Compare results of the two optimization methods
int retval = 0;
for (unsigned i = 0; i < model->sizeParams(); ++i)
for (unsigned i = 0; i < model.sizeParams(); ++i)
{
if (!isEqual(results[i], model->param()[i], 100 * tol))
if (!isEqual(results[i], model.param()[i], 100 * tol))
--retval;
}

Expand All @@ -155,7 +155,5 @@ int main()
}

delete[] results;
delete idas;
delete model;
return 0;
}
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