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[DLG] Update ipopt_optimize_nlp_cppad.h x3
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@diegolodares
diegolodares committed Nov 22, 2023
1 parent 0938424 commit 6e6551e
Showing 1 changed file with 106 additions and 41 deletions.
147 changes: 106 additions & 41 deletions lion/math/ipopt_optimize_nlp_cppad.h
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Original file line number Diff line number Diff line change
@@ -1,50 +1,94 @@
#ifndef IPOPT_OPTIMIZE_NLP_CPPAD_H
#define IPOPT_OPTIMIZE_NLP_CPPAD_H
#ifndef LION_MATH_IPOPT_OPTIMIZE_NLP_CPPAD_H
#define LION_MATH_IPOPT_OPTIMIZE_NLP_CPPAD_H
#pragma once

#include <string>
#include <coin-or/IpSmartPtr.hpp>
#include <coin-or/IpIpoptApplication.hpp>
#include <cppad/cppad.hpp>

#include "lion/foundation/lion_exception.h"

#include "matrix_extensions.h"
#include "optimization_result.h"
#include "ipopt_nlp_complete_output.h"

#include "detail/ipopt_nlp_cppad.h"
#include "coin-or/IpSmartPtr.hpp"
#include "coin-or/IpIpoptApplication.hpp"
#include "cppad/cppad.hpp"

#include "lion/foundation/lion_exception.h"
#include "lion/math/matrix_extensions.h"
#include "lion/math/optimization_result.h"
#include "lion/math/ipopt_nlp_complete_output.h"
#include "lion/math/detail/ipopt_nlp_cppad.h"


//
// Defines functions to optimize, via Ipopt + CppAD, NLP's represented by
// either:
//
// a) Function objects that must define a member operator
// "void operator()(ADvector &fg, const ADvector &x)", in which "x"
// represents the optimization variables and "fg" is the resulting
// concatenation of "[scalar_cost; vector_of_constraints]".
//
// b) Instances of class "CppAD::ADFun<Base>", which have been
// taped adequately so that their "Forward(0, x)" call returns
// the said "[scalar_cost; vector_of_constraints]".
//

namespace lioncpp {

namespace detail::ipopt_optimize_nlp_cppad {

template<class FG_eval>
//
// A type_trait to deduce the "ADvector" type that "ipopt_optimize_nlp_cppad"
// will use to solve the NLP represented by the "FG_eval" functor. If "FG_eval"
// declares a member "ADvector" typedef, then it will use that one. When the
// "FG_eval" is a "CppAD::ADFun<Base>", it will use "std::vector<CppAD::AD<Base> >".
// Else, it will use "std::vector<CppAD::AD<scalar> >".
//

template<class FG_eval, typename = void>
struct ADvector_type
{
using type = std::vector<CppAD::AD<scalar> >;
};

template<class FG_eval>
struct ADvector_type<FG_eval, std::void_t<typename FG_eval::ADvector> >
{
using type = typename FG_eval::ADvector;
};

template<typename T>
struct ADvector_type<CppAD::ADFun<T> >
template<typename Base>
struct ADvector_type<CppAD::ADFun<Base> >
{
using type = std::vector<CppAD::AD<T> >;
using type = std::vector<CppAD::AD<Base> >;
};

template<class FG_eval>
using ADvector_type_t = typename ADvector_type<FG_eval>::type;
using ADvector_type = typename ADvector_type<FG_eval>::type;

} // end namespace detail::ipopt_optimize_nlp_cppad


//! Helper function to set several options from a string into an Ipopt Application
void set_ipopt_app_options_from_string(Ipopt::SmartPtr<Ipopt::IpoptApplication>& app, const std::string& options, bool& retape, bool& sparse_forward, bool& sparse_reverse);

//! Optimize a NLP computed from the functor fg_eval
template<typename ADvector = detail::ipopt_optimize_nlp_cppad::ADvector_type_t<FG_eval>, class Dvector, class FG_eval>
void ipopt_optimize_nlp_cppad(const std::string& options, const Dvector& xi, const Dvector& xl, const Dvector& xu, const Dvector& gl, const Dvector& gu,
FG_eval& fg_eval, Optimization_result<Dvector>& solution)
// fwd. decl. of a helper function to set several options from a string into an Ipopt Application
void set_ipopt_app_options_from_string(Ipopt::SmartPtr<Ipopt::IpoptApplication> &app,
const std::string &options,
bool &retape,
bool &sparse_forward,
bool &sparse_reverse);


template<typename Dvector,
class FG_eval,
typename ADvector = detail::ipopt_optimize_nlp_cppad::ADvector_type_t<FG_eval> >
void ipopt_optimize_nlp_cppad(const std::string &options,
const Dvector &xi,
const Dvector &xl,
const Dvector &xu,
const Dvector &gl,
const Dvector &gu,
FG_eval &fg_eval,
Optimization_result<Dvector> &solution)
{
//
// Optimizes an NLP represented by functor "fg_eval".
//

bool ok = true;

CPPAD_ASSERT_KNOWN(
Expand Down Expand Up @@ -92,8 +136,7 @@ void ipopt_optimize_nlp_cppad(const std::string& options, const Dvector& xi, con
retape,
sparse_forward,
sparse_reverse,
solution
);
solution);

// Run the IpoptApplication
app->OptimizeTNLP(cppad_nlp);
Expand All @@ -104,11 +147,25 @@ void ipopt_optimize_nlp_cppad(const std::string& options, const Dvector& xi, con
}


template <class Dvector, class FG_eval>
void ipopt_optimize_nlp_cppad(const std::string& options, const Dvector& xi, const Dvector& xl, const Dvector& xu, const Dvector& gl, const Dvector& gu,
const Dvector& lambda_i, const Dvector& zl_i, const Dvector& zu_i, FG_eval& fg_eval, Optimization_result<Dvector>& solution)
template<typename Dvector,
class FG_eval,
typename ADvector = detail::ipopt_optimize_nlp_cppad::ADvector_type_t<FG_eval> >
void ipopt_optimize_nlp_cppad(const std::string &options,
const Dvector &xi,
const Dvector &xl,
const Dvector &xu,
const Dvector &gl,
const Dvector &gu,
const Dvector &lambda_i,
const Dvector &zl_i,
const Dvector &zu_i,
FG_eval &fg_eval,
Optimization_result<Dvector> &solution)
{
typedef typename FG_eval::ADvector ADvector;
//
// Optimizes an NLP represented by functor "fg_eval",
// with warm-start.
//

bool ok = true;

Expand Down Expand Up @@ -180,7 +237,11 @@ void ipopt_optimize_nlp_cppad(const std::string& options, const Dvector& xi, con
}


inline void set_ipopt_app_options_from_string(Ipopt::SmartPtr<Ipopt::IpoptApplication>& app, const std::string& options, bool& retape, bool& sparse_forward, bool& sparse_reverse)
inline void set_ipopt_app_options_from_string(Ipopt::SmartPtr<Ipopt::IpoptApplication> &app,
const std::string &options,
bool &retape,
bool &sparse_forward,
bool &sparse_reverse)
{
// process the options argument
size_t begin_1, end_1, begin_2, end_2, begin_3, end_3;
Expand Down Expand Up @@ -299,17 +360,22 @@ inline void set_ipopt_app_options_from_string(Ipopt::SmartPtr<Ipopt::IpoptApplic
}


template <class Dvector, class FG_eval>
NLP_complete_output ipopt_compute_complete_nlp_cppad(
const size_t nx,
const size_t ng,
FG_eval& fg_eval,
const std::vector<double>& x,
const std::vector<double>& lambda)
template<typename Dvector,
class FG_eval,
typename ADvector = detail::ipopt_optimize_nlp_cppad::ADvector_type_t<FG_eval> >
NLP_complete_output ipopt_compute_complete_nlp_cppad(std::size_t nx,
std::size_t ng,
FG_eval &fg_eval,
const std::vector<scalar> &x,
const std::vector<scalar> &lambda)
{
//
// Completely characterizes the NLP represented by functor "fg_eval",
// at point "x" with Lagrange multipliers "lambda"
//

bool ok = true;

typedef typename FG_eval::ADvector ADvector;
Optimization_result<Dvector> solution;

bool retape = false, sparse_forward = true, sparse_reverse = false;
Expand All @@ -330,8 +396,7 @@ NLP_complete_output ipopt_compute_complete_nlp_cppad(
retape,
sparse_forward,
sparse_reverse,
solution
);
solution);

NLP_complete_output output;

Expand Down

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