#include "IpIpoptApplication.hpp"
#include "IpSolveStatistics.hpp"
#include "program.hpp"
#include <iostream>
#include <time.h>
#include <armadillo>
#include "GPM.h"
using namespace Ipopt;
using namespace arma;

int ipopt_apply(
	/* input */
	int var_num, int constraint_num, int nnz_jac, int nnz_h, vec x00, vec Fmin0, vec Fmax0, vec Zmin0, vec Zmax0,
	auxdata1 auxdata00, mat W_phase00, mat deta_t_in_col00, int Col_total00, int length_state00, int length_control00,
	Grad_struct_info0 Grad_struct_info00, int length_integral00, double eps00, mat D_phase00, uvec first_col00, int length_path00,
	int length_event00, sp_mat Jacb_struct00, field<mat> D_phasei0, uvec N_Col00, sp_mat I_Z_struct00, int interval_num00, int length_Z00, 
	int length_parameter00, Jacb_struct_info0 Jacb_struct_info00, char *deri_supplier00, int length_F000, NLP1 NLP,
	int(*Dynamics_ptr00)(mat* StateVar, mat* ControlVar, auxdata1 auxdata, mat* dState, mat* integrand, mat* path),
	int(*Objective_ptr00)(input0* input, double* output, mat* event),
	vec* z_ans, vec* zl_temp, vec* zu_temp, vec* lambda_temp, int* iter_num, double *cpu_time
	){

	// 相关参量赋值
	get_var_num(var_num);
	get_constraint_num(constraint_num);
	get_nnz_jac(nnz_jac);
	get_nnz_h(nnz_h);
	get_x0(x00);
	get_bounds(Fmin0, Fmax0, Zmin0, Zmax0);
	get_data(auxdata00, W_phase00, deta_t_in_col00, Col_total00, length_state00, length_control00,
			Grad_struct_info00,  length_integral00, eps00, D_phase00, first_col00,  length_path00,
			length_event00, Jacb_struct00, D_phasei0, N_Col00, I_Z_struct00, interval_num00, length_Z00, 
			length_parameter00, Jacb_struct_info00, deri_supplier00, length_F000, Dynamics_ptr00, Objective_ptr00);

	// 创建nlp的实例
	SmartPtr<TNLP> mynlp = new Program();

	// 创建IpoptApplication的实例
	//
	// 使用Factory，因为这使我们可以使用Ipopt Windows DLL编译此示例
	SmartPtr<IpoptApplication> app = IpoptApplicationFactory();

	// IPOPT选项设置
	app->Options()->SetStringValue("mu_strategy", NLP.ipoptoptions.mu_strategy);
	app->Options()->SetStringValue("linear_solver", NLP.ipoptoptions.linear_solver);	// 求解器，ma27、ma57、ma77、ma86、ma97、pardiso、wsmp、mumps
	// Convergence
	app->Options()->SetNumericValue("tol", NLP.ipoptoptions.tolerance);					// 算法的收敛容限
	app->Options()->SetIntegerValue("max_iter", 3000);									// 最大迭代次数
#ifndef LUOYC20220604
	app->Options()->SetNumericValue("max_cpu_time", 200000);									// 最大CPU迭代时间
#else
	app->Options()->SetNumericValue("max_cpu_time", 1);									// 最大CPU迭代时间
#endif
	app->Options()->SetNumericValue("acceptable_tol", 1e-5);							// 相对可接受的收敛误差
	app->Options()->SetIntegerValue("acceptable_iter", 10);								// 相对可接受的迭代次数
	//app->Options()->SetNumericValue("acceptable_obj_change_tol", 1e-3);				// 目标函数的相对变化(按 Max(1,|f(x)|)缩放)小于这个值，则终止
	// NLP Scaling
	// app->Options()->SetStringValue("nlp_scaling_method", "gradient-based");			// 问题的缩放方法
	// NLP
	app->Options()->SetNumericValue("nlp_lower_bound_inf", -1e19);						// 小于或等于此值的任何界限都将被视为-inf
	app->Options()->SetNumericValue("nlp_upper_bound_inf", 1e19);						// 大于或等于此值的任何界限都将被视为inf
	// output
	app->Options()->SetIntegerValue("print_level", 0);									// 控制台的显示等级，0~12
	//app->Options()->SetStringValue("output_file", "ipopt_result.out");				// 设置输出文件
	app->Options()->SetStringValue("print_user_options", "no");							// 打印用户设置的所有选项的列表 
	app->Options()->SetStringValue("print_options_documentation", "no");				// 打印所有可用算法选项 
	app->Options()->SetStringValue("print_timing_statistics", "no");					// 打印所选任务的CPU使用情况
	//app->Options()->SetStringValue("option_file_name", "ipopt_option");				// 指定选项文件的名称
	//app->Options()->SetStringValue("inf_pr_output", "internal");						// 选择“internal”打印出该公式的约束违反。 // 使用“original”打印原始NLP中的真正约束违反
	//app->Options()->SetIntegerValue("print_frequency_iter", 1);						// 打印汇总迭代输出行的迭代频率 
	//
	app->Options()->SetStringValue("hessian_approximation", NLP.ipoptoptions.hessian_approximation);	// 打印汇总迭代输出行的迭代频率 
	
	ApplicationReturnStatus status;
  
	// 初始化
	status = app->Initialize();
	if (status != Solve_Succeeded) {
		std::cout << std::endl << std::endl << "*** 初 始 化 错 误 ! ***" << std::endl;
		return (int) status;
	}
	
	// 进行优化
	status = app->OptimizeTNLP(mynlp);

	Status_Display(status);
	int a = status;
	*z_ans = Program::x;
	*zl_temp = Program::z_L;
	*zu_temp = Program::z_U;
	*lambda_temp = Program::lambda;
	*cpu_time = app->Statistics()->TotalCPUTime();
	Index iter_count = app->Statistics()->IterationCount();
	*iter_num = iter_count;
	/*
	if (a == -13 || a == -199) {
		*cpu_time = 0;
		Index iter_count = 0;
		*iter_num = iter_count;
	}
	else {
		*cpu_time = app->Statistics()->TotalCPUTime();
		Index iter_count = app->Statistics()->IterationCount();
		*iter_num = iter_count;
	}
	*/
	return (int) status;
}