使用 CppAD 时，IPOPT 不服从约束但不记录违规
IPOPT does not obey constraints but does not record the violation when using CppAD
我正在尝试评估两个五阶多项式（x 和 y 位置各一个）的系数和时间，以在连接初始位置、速度时最大限度地减少工作量和时间（objective 函数） ，以及以 0 速度（等式约束）指向所需的最终位置和方向。这是代码：
#include <vector>
#include <cppad/cppad.hpp>
#include <cppad/ipopt/solve.hpp>
using CppAD::AD;
typedef struct {
double x, y, theta, linear_velocity;
} Waypoint;
typedef std::vector<Waypoint> WaypointList;
struct TrajectoryConfig {
//! gain on accumulated jerk term in cost function
double Kj;
//! gain on time term in cost function
double Kt;
//! gain on terminal velocity term in cost function
double Kv;
};
class Trajectory {
public:
explicit Trajectory(TrajectoryConfig config);
~Trajectory();
void updateConfigs(TrajectoryConfig config);
void solve(WaypointList waypoints);
private:
//! solution vector
std::vector<double> solution_;
//! gain on accumulated jerk term in cost function
double Kj_;
//! gain on time term in cost function
double Kt_;
//! gain on terminal velocity term in cost function
double Kv_;
};
/*
Trajectory(TrajectoryConfig)
class constructor. Initializes class given configuration struct
*/
Trajectory::Trajectory(TrajectoryConfig config) {
Kj_ = config.Kj;
Kt_ = config.Kt;
Kv_ = config.Kv;
}
Trajectory::~Trajectory() {
std::cerr << "Trajectory Destructor!" << std::endl;
}
enum Indices { A0 = 0, A1, A2, A3, A4, A5, B0, B1, B2, B3, B4, B5, T };
class FGradEval {
public:
size_t M_;
// gains on cost;
double Kj_, Kt_;
// constructor
FGradEval(double Kj, double Kt) {
M_ = 13; // no. of parameters per trajectory segment: 2 x 6 coefficients + 1 time
Kj_ = Kj;
Kt_ = Kt;
}
typedef CPPAD_TESTVECTOR(AD<double>) ADvector;
void operator()(ADvector& fgrad, const ADvector& vars) {
fgrad[0] = 0;
AD<double> accum_jerk;
AD<double> a0, a1, a2, a3, a4, a5;
AD<double> b0, b1, b2, b3, b4, b5;
AD<double> T, T2, T3, T4, T5;
AD<double> x, y, vx, vy;
size_t offset = 1;
a0 = vars[Indices::A0];
a1 = vars[Indices::A1];
a2 = vars[Indices::A2];
a3 = vars[Indices::A3];
a4 = vars[Indices::A4];
a5 = vars[Indices::A5];
b0 = vars[Indices::B0];
b1 = vars[Indices::B1];
b2 = vars[Indices::B2];
b3 = vars[Indices::B3];
b4 = vars[Indices::B4];
b5 = vars[Indices::B5];
T = vars[Indices::T];
T2 = T*T;
T3 = T*T2;
T4 = T*T3;
T5 = T*T4;
x = a0 + a1*T + a2*T2 + a3*T3 + a4*T4 + a5*T5;
y = b0 + b1*T + b2*T2 + b3*T3 + b4*T4 + b5*T5;
vx = a1 + 2*a2*T + 3*a3*T2 + 4*b4*T3 + 5*a5*T4;
vy = b1 + 2*b2*T + 3*b3*T2 + 4*b4*T3 + 5*b5*T4;
//! cost-terms
//! accum_jerk is the analytic integral of int_0^T (jerk_x^2 + jerk_y^2) dt
accum_jerk = 36 * T * (a3*a3 + b3*b3) + 144 * T2 * (a3*a4 + b3*b4) + T3 * (240*(a3*a5 + b3*b5) + 192*(a4*a4 + b4*b4))
+ 720 * T4 * (a4*a5 + b4*b5) + 720 * T5 * (a5*a5 + b5*b5);
fgrad[0] += Kj_ * accum_jerk;
fgrad[0] += Kt_ * T;
//! initial equality constraints
fgrad[offset] = vars[Indices::A0];
fgrad[1 + offset] = vars[Indices::B0];
fgrad[2 + offset] = vars[Indices::A1];
fgrad[3 + offset] = vars[Indices::B1];
offset += 4;
//! terminal inequality constraints
fgrad[offset] = x;
fgrad[offset + 1] = y;
fgrad[offset + 2] = vx;
fgrad[offset + 3] = vy;
}
};
void Trajectory::solve(WaypointList waypoints) {
if (waypoints.size() != 2) {
std::cerr << "Trajectory::solve - Function requires 2 waypoints." << std::endl;
return;
}
//! status flag for solution
bool ok;
//! typedef for ipopt/cppad
typedef CPPAD_TESTVECTOR(double) Dvector;
//! no. of variables for optimization problem
size_t n_vars = 13;
//! no. of constraints
size_t n_cons = 4 * 2; // the start and final waypoint each contribute 4 constraints (x, y, theta, v) -> (x, y, vx, vy)
//! create vector container for optimizer solution
//! and initialize it to zero
Dvector vars(n_vars);
for (size_t i = 0; i < n_vars; i++) {
vars[i] = 0;
}
//! set initial state (this will only determine the first two coefficients of the initial polynomials)
double v = (fabs(waypoints[0].linear_velocity) < 1e-3)
? 1e-3 : waypoints[0].linear_velocity;
vars[Indices::A0] = waypoints[0].x;
vars[Indices::B0] = waypoints[0].y;
vars[Indices::A1] = v * cos(waypoints[0].theta);
vars[Indices::B1] = v * sin(waypoints[0].theta);
vars[Indices::T] = 0;
//! there are no explicit bounds on vars, so set to something large for the optimizer
//! we could perhaps put bounds on the coeffs corresponding to acc, jerk, snap, ..
Dvector vars_lb(n_vars);
Dvector vars_ub(n_vars);
for (size_t i = 0; i < n_vars; i++) {
vars_lb[i] = -1e10;
vars_ub[i] = 1e10;
}
//! time must be non-negative!
vars_lb[Indices::T] = 0;
//! set the bounds on the constraints
Dvector cons_lb(n_cons);
Dvector cons_ub(n_cons);
//! offset term on index
size_t offset = 0;
//! initial equality constraint - we must start from where we are!
cons_lb[0] = waypoints[0].x;
cons_ub[0] = waypoints[0].x;
cons_lb[1] = waypoints[0].y;
cons_ub[1] = waypoints[0].y;
cons_lb[2] = v * cos(waypoints[0].theta);
cons_ub[2] = v * cos(waypoints[0].theta);
cons_lb[3] = v * sin(waypoints[0].theta);
cons_ub[3] = v * sin(waypoints[0].theta);
offset += 4;
//! terminal point
cons_lb[offset] = waypoints[1].x;
cons_ub[offset] = waypoints[1].x;
cons_lb[offset + 1] = waypoints[1].y;
cons_ub[offset + 1] = waypoints[1].y;
cons_lb[offset + 2] = 1e-3 * cos(waypoints[1].theta);
cons_ub[offset + 2] = 1e-3 * cos(waypoints[1].theta);
cons_lb[offset + 3] = 1e-3 * sin(waypoints[1].theta);
cons_ub[offset + 3] = 1e-3 * sin(waypoints[1].theta);
//! create instance of objective function class
FGradEval fg_eval(Kj_, Kt_);
//! IPOPT INITIALIZATION
std::string options;
options += "Integer print_level 5\n";
options += "Sparse true forward\n";
options += "Sparse true reverse\n";
options += "Integer max_iter 100\n";
// options += "Numeric tol 1e-4\n";
//! compute the solution
CppAD::ipopt::solve_result<Dvector> solution;
//! solve
CppAD::ipopt::solve<Dvector, FGradEval>(
options, vars, vars_lb, vars_ub, cons_lb, cons_ub, fg_eval, solution);
//! check if the solver was successful
ok = solution.status == CppAD::ipopt::solve_result<Dvector>::success;
//! if the solver was unsuccessful, exit
//! this case will be handled by calling method
if (!ok) {
std::cerr << "Trajectory::solve - Failed to find a solution!" << std::endl;
return;
}
//! (DEBUG) output the final cost
std::cout << "Final Cost: " << solution.obj_value << std::endl;
//! populate output with argmin vector
for (size_t i = 0; i < n_vars; i++) {
solution_.push_back(solution.x[i]);
}
return;
}
我遇到问题的地方如下：
- 初始等式约束（起始位置、速度和方向）得到支持，而终端速度约束则没有。该算法终止于正确的最终 (x,y,angle)，但速度不为零。我已经查看了代码，但我不明白为什么会遵守端点的位置和方向而速度不会。我怀疑我对等式约束的定义不是我想的那样。
- 问题没有规律地收敛，但这似乎是一个定义相当简单的问题（见输出）
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
******************************************************************************
This is Ipopt version 3.11.9, running with linear solver mumps.
NOTE: Other linear solvers might be more efficient (see Ipopt documentation).
Number of nonzeros in equality constraint Jacobian...: 30
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 23
Total number of variables............................: 13
variables with only lower bounds: 0
variables with lower and upper bounds: 13
variables with only upper bounds: 0
Total number of equality constraints.................: 8
Total number of inequality constraints...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 9.9999900e-03 1.00e+00 5.00e-04 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 5.9117705e-02 1.00e+00 1.20e+02 -1.0 5.36e+07 - 1.04e-05 7.63e-06f 18
2 1.1927070e+00 1.00e+00 2.62e+06 -1.0 9.21e+05 -4.0 6.16e-15 2.29e-23H 1
3 2.9689692e-01 1.00e+00 1.80e+05 -1.0 2.24e+13 - 1.83e-07 8.42e-10f 20
4r 2.9689692e-01 1.00e+00 1.00e+03 -0.0 0.00e+00 - 0.00e+00 4.58e-07R 11
5r 2.1005820e+01 9.99e-01 5.04e+02 -0.0 6.60e-02 - 9.90e-01 4.95e-01f 2
6r 7.7118141e+04 9.08e-01 5.18e+03 -0.0 2.09e+00 - 4.21e-01 1.00e+00f 1
7r 1.7923891e+04 7.82e-01 1.54e+03 -0.0 3.63e+00 - 9.90e-01 1.00e+00f 1
8r 5.9690221e+03 5.41e-01 5.12e+02 -0.0 2.92e+00 - 9.90e-01 1.00e+00f 1
9r 4.6855625e+03 5.54e-01 1.95e+02 -0.0 5.14e-01 - 9.92e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10r 8.4901226e+03 5.55e-01 5.18e+01 -0.0 2.24e-01 - 1.00e+00 1.00e+00f 1
Number of Iterations....: 10
(scaled) (unscaled)
Objective...............: 8.4901225582208808e+03 8.4901225582208808e+03
Dual infeasibility......: 6.3613117039244315e+06 6.3613117039244315e+06
Constraint violation....: 5.5503677023620179e-01 5.5503677023620179e-01
Complementarity.........: 9.9999982900301554e-01 9.9999982900301554e-01
Overall NLP error.......: 6.3613117039244315e+06 6.3613117039244315e+06
Number of objective function evaluations = 43
Number of objective gradient evaluations = 6
Number of equality constraint evaluations = 71
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 12
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 10
Total CPU secs in IPOPT (w/o function evaluations) = 0.006
Total CPU secs in NLP function evaluations = 0.001
EXIT: Maximum Number of Iterations Exceeded.
我并不是专门为我的问题寻找答案。我希望得到一些关于为什么我的问题可能无法按预期工作的建议。具体来说，我的约束是否有意义，如定义的那样？变量初始化是否正确？
问题出在以下几行：
x = a0 + a1*T + a2*T2 + a3*T3 + a4*T4 + a5*T5;
y = b0 + b1*T + b2*T2 + b3*T3 + b4*T4 + b5*T5;
vx = a1 + 2*a2*T + 3*a3*T2 + 4*b4*T3 + 5*a5*T4;
vy = b1 + 2*b2*T + 3*b3*T2 + 4*b4*T3 + 5*b5*T4;
具体来说，
vx = a1 + 2*a2*T + 3*a3*T2 + 4*b4*T3 + 5*a5*T4;
应该是
vx = a1 + 2*a2*T + 3*a3*T2 + 4*a4*T3 + 5*a5*T4;
基于 a 到 x 坐标和 b 到 y 坐标的映射。
这解决了违反约束的问题。
关于convergence/feasibility的问题，我发现确保初始猜测在可行集中（服从等式约束）解决了这个问题；在修复初始条件后，优化器性能的度量（inf_pr 和 inf_du 等...）要小得多。
我正在尝试评估两个五阶多项式（x 和 y 位置各一个）的系数和时间，以在连接初始位置、速度时最大限度地减少工作量和时间（objective 函数） ，以及以 0 速度（等式约束）指向所需的最终位置和方向。这是代码：
#include <vector>
#include <cppad/cppad.hpp>
#include <cppad/ipopt/solve.hpp>
using CppAD::AD;
typedef struct {
double x, y, theta, linear_velocity;
} Waypoint;
typedef std::vector<Waypoint> WaypointList;
struct TrajectoryConfig {
//! gain on accumulated jerk term in cost function
double Kj;
//! gain on time term in cost function
double Kt;
//! gain on terminal velocity term in cost function
double Kv;
};
class Trajectory {
public:
explicit Trajectory(TrajectoryConfig config);
~Trajectory();
void updateConfigs(TrajectoryConfig config);
void solve(WaypointList waypoints);
private:
//! solution vector
std::vector<double> solution_;
//! gain on accumulated jerk term in cost function
double Kj_;
//! gain on time term in cost function
double Kt_;
//! gain on terminal velocity term in cost function
double Kv_;
};
/*
Trajectory(TrajectoryConfig)
class constructor. Initializes class given configuration struct
*/
Trajectory::Trajectory(TrajectoryConfig config) {
Kj_ = config.Kj;
Kt_ = config.Kt;
Kv_ = config.Kv;
}
Trajectory::~Trajectory() {
std::cerr << "Trajectory Destructor!" << std::endl;
}
enum Indices { A0 = 0, A1, A2, A3, A4, A5, B0, B1, B2, B3, B4, B5, T };
class FGradEval {
public:
size_t M_;
// gains on cost;
double Kj_, Kt_;
// constructor
FGradEval(double Kj, double Kt) {
M_ = 13; // no. of parameters per trajectory segment: 2 x 6 coefficients + 1 time
Kj_ = Kj;
Kt_ = Kt;
}
typedef CPPAD_TESTVECTOR(AD<double>) ADvector;
void operator()(ADvector& fgrad, const ADvector& vars) {
fgrad[0] = 0;
AD<double> accum_jerk;
AD<double> a0, a1, a2, a3, a4, a5;
AD<double> b0, b1, b2, b3, b4, b5;
AD<double> T, T2, T3, T4, T5;
AD<double> x, y, vx, vy;
size_t offset = 1;
a0 = vars[Indices::A0];
a1 = vars[Indices::A1];
a2 = vars[Indices::A2];
a3 = vars[Indices::A3];
a4 = vars[Indices::A4];
a5 = vars[Indices::A5];
b0 = vars[Indices::B0];
b1 = vars[Indices::B1];
b2 = vars[Indices::B2];
b3 = vars[Indices::B3];
b4 = vars[Indices::B4];
b5 = vars[Indices::B5];
T = vars[Indices::T];
T2 = T*T;
T3 = T*T2;
T4 = T*T3;
T5 = T*T4;
x = a0 + a1*T + a2*T2 + a3*T3 + a4*T4 + a5*T5;
y = b0 + b1*T + b2*T2 + b3*T3 + b4*T4 + b5*T5;
vx = a1 + 2*a2*T + 3*a3*T2 + 4*b4*T3 + 5*a5*T4;
vy = b1 + 2*b2*T + 3*b3*T2 + 4*b4*T3 + 5*b5*T4;
//! cost-terms
//! accum_jerk is the analytic integral of int_0^T (jerk_x^2 + jerk_y^2) dt
accum_jerk = 36 * T * (a3*a3 + b3*b3) + 144 * T2 * (a3*a4 + b3*b4) + T3 * (240*(a3*a5 + b3*b5) + 192*(a4*a4 + b4*b4))
+ 720 * T4 * (a4*a5 + b4*b5) + 720 * T5 * (a5*a5 + b5*b5);
fgrad[0] += Kj_ * accum_jerk;
fgrad[0] += Kt_ * T;
//! initial equality constraints
fgrad[offset] = vars[Indices::A0];
fgrad[1 + offset] = vars[Indices::B0];
fgrad[2 + offset] = vars[Indices::A1];
fgrad[3 + offset] = vars[Indices::B1];
offset += 4;
//! terminal inequality constraints
fgrad[offset] = x;
fgrad[offset + 1] = y;
fgrad[offset + 2] = vx;
fgrad[offset + 3] = vy;
}
};
void Trajectory::solve(WaypointList waypoints) {
if (waypoints.size() != 2) {
std::cerr << "Trajectory::solve - Function requires 2 waypoints." << std::endl;
return;
}
//! status flag for solution
bool ok;
//! typedef for ipopt/cppad
typedef CPPAD_TESTVECTOR(double) Dvector;
//! no. of variables for optimization problem
size_t n_vars = 13;
//! no. of constraints
size_t n_cons = 4 * 2; // the start and final waypoint each contribute 4 constraints (x, y, theta, v) -> (x, y, vx, vy)
//! create vector container for optimizer solution
//! and initialize it to zero
Dvector vars(n_vars);
for (size_t i = 0; i < n_vars; i++) {
vars[i] = 0;
}
//! set initial state (this will only determine the first two coefficients of the initial polynomials)
double v = (fabs(waypoints[0].linear_velocity) < 1e-3)
? 1e-3 : waypoints[0].linear_velocity;
vars[Indices::A0] = waypoints[0].x;
vars[Indices::B0] = waypoints[0].y;
vars[Indices::A1] = v * cos(waypoints[0].theta);
vars[Indices::B1] = v * sin(waypoints[0].theta);
vars[Indices::T] = 0;
//! there are no explicit bounds on vars, so set to something large for the optimizer
//! we could perhaps put bounds on the coeffs corresponding to acc, jerk, snap, ..
Dvector vars_lb(n_vars);
Dvector vars_ub(n_vars);
for (size_t i = 0; i < n_vars; i++) {
vars_lb[i] = -1e10;
vars_ub[i] = 1e10;
}
//! time must be non-negative!
vars_lb[Indices::T] = 0;
//! set the bounds on the constraints
Dvector cons_lb(n_cons);
Dvector cons_ub(n_cons);
//! offset term on index
size_t offset = 0;
//! initial equality constraint - we must start from where we are!
cons_lb[0] = waypoints[0].x;
cons_ub[0] = waypoints[0].x;
cons_lb[1] = waypoints[0].y;
cons_ub[1] = waypoints[0].y;
cons_lb[2] = v * cos(waypoints[0].theta);
cons_ub[2] = v * cos(waypoints[0].theta);
cons_lb[3] = v * sin(waypoints[0].theta);
cons_ub[3] = v * sin(waypoints[0].theta);
offset += 4;
//! terminal point
cons_lb[offset] = waypoints[1].x;
cons_ub[offset] = waypoints[1].x;
cons_lb[offset + 1] = waypoints[1].y;
cons_ub[offset + 1] = waypoints[1].y;
cons_lb[offset + 2] = 1e-3 * cos(waypoints[1].theta);
cons_ub[offset + 2] = 1e-3 * cos(waypoints[1].theta);
cons_lb[offset + 3] = 1e-3 * sin(waypoints[1].theta);
cons_ub[offset + 3] = 1e-3 * sin(waypoints[1].theta);
//! create instance of objective function class
FGradEval fg_eval(Kj_, Kt_);
//! IPOPT INITIALIZATION
std::string options;
options += "Integer print_level 5\n";
options += "Sparse true forward\n";
options += "Sparse true reverse\n";
options += "Integer max_iter 100\n";
// options += "Numeric tol 1e-4\n";
//! compute the solution
CppAD::ipopt::solve_result<Dvector> solution;
//! solve
CppAD::ipopt::solve<Dvector, FGradEval>(
options, vars, vars_lb, vars_ub, cons_lb, cons_ub, fg_eval, solution);
//! check if the solver was successful
ok = solution.status == CppAD::ipopt::solve_result<Dvector>::success;
//! if the solver was unsuccessful, exit
//! this case will be handled by calling method
if (!ok) {
std::cerr << "Trajectory::solve - Failed to find a solution!" << std::endl;
return;
}
//! (DEBUG) output the final cost
std::cout << "Final Cost: " << solution.obj_value << std::endl;
//! populate output with argmin vector
for (size_t i = 0; i < n_vars; i++) {
solution_.push_back(solution.x[i]);
}
return;
}
我遇到问题的地方如下：
- 初始等式约束（起始位置、速度和方向）得到支持，而终端速度约束则没有。该算法终止于正确的最终 (x,y,angle)，但速度不为零。我已经查看了代码，但我不明白为什么会遵守端点的位置和方向而速度不会。我怀疑我对等式约束的定义不是我想的那样。
- 问题没有规律地收敛，但这似乎是一个定义相当简单的问题（见输出）
****************************************************************************** This program contains Ipopt, a library for large-scale nonlinear optimization. Ipopt is released as open source code under the Eclipse Public License (EPL). For more information visit http://projects.coin-or.org/Ipopt ****************************************************************************** This is Ipopt version 3.11.9, running with linear solver mumps. NOTE: Other linear solvers might be more efficient (see Ipopt documentation). Number of nonzeros in equality constraint Jacobian...: 30 Number of nonzeros in inequality constraint Jacobian.: 0 Number of nonzeros in Lagrangian Hessian.............: 23 Total number of variables............................: 13 variables with only lower bounds: 0 variables with lower and upper bounds: 13 variables with only upper bounds: 0 Total number of equality constraints.................: 8 Total number of inequality constraints...............: 0 inequality constraints with only lower bounds: 0 inequality constraints with lower and upper bounds: 0 inequality constraints with only upper bounds: 0 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 0 9.9999900e-03 1.00e+00 5.00e-04 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0 1 5.9117705e-02 1.00e+00 1.20e+02 -1.0 5.36e+07 - 1.04e-05 7.63e-06f 18 2 1.1927070e+00 1.00e+00 2.62e+06 -1.0 9.21e+05 -4.0 6.16e-15 2.29e-23H 1 3 2.9689692e-01 1.00e+00 1.80e+05 -1.0 2.24e+13 - 1.83e-07 8.42e-10f 20 4r 2.9689692e-01 1.00e+00 1.00e+03 -0.0 0.00e+00 - 0.00e+00 4.58e-07R 11 5r 2.1005820e+01 9.99e-01 5.04e+02 -0.0 6.60e-02 - 9.90e-01 4.95e-01f 2 6r 7.7118141e+04 9.08e-01 5.18e+03 -0.0 2.09e+00 - 4.21e-01 1.00e+00f 1 7r 1.7923891e+04 7.82e-01 1.54e+03 -0.0 3.63e+00 - 9.90e-01 1.00e+00f 1 8r 5.9690221e+03 5.41e-01 5.12e+02 -0.0 2.92e+00 - 9.90e-01 1.00e+00f 1 9r 4.6855625e+03 5.54e-01 1.95e+02 -0.0 5.14e-01 - 9.92e-01 1.00e+00f 1 iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls 10r 8.4901226e+03 5.55e-01 5.18e+01 -0.0 2.24e-01 - 1.00e+00 1.00e+00f 1 Number of Iterations....: 10 (scaled) (unscaled) Objective...............: 8.4901225582208808e+03 8.4901225582208808e+03 Dual infeasibility......: 6.3613117039244315e+06 6.3613117039244315e+06 Constraint violation....: 5.5503677023620179e-01 5.5503677023620179e-01 Complementarity.........: 9.9999982900301554e-01 9.9999982900301554e-01 Overall NLP error.......: 6.3613117039244315e+06 6.3613117039244315e+06 Number of objective function evaluations = 43 Number of objective gradient evaluations = 6 Number of equality constraint evaluations = 71 Number of inequality constraint evaluations = 0 Number of equality constraint Jacobian evaluations = 12 Number of inequality constraint Jacobian evaluations = 0 Number of Lagrangian Hessian evaluations = 10 Total CPU secs in IPOPT (w/o function evaluations) = 0.006 Total CPU secs in NLP function evaluations = 0.001 EXIT: Maximum Number of Iterations Exceeded.
我并不是专门为我的问题寻找答案。我希望得到一些关于为什么我的问题可能无法按预期工作的建议。具体来说，我的约束是否有意义，如定义的那样？变量初始化是否正确？
问题出在以下几行：
x = a0 + a1*T + a2*T2 + a3*T3 + a4*T4 + a5*T5;
y = b0 + b1*T + b2*T2 + b3*T3 + b4*T4 + b5*T5;
vx = a1 + 2*a2*T + 3*a3*T2 + 4*b4*T3 + 5*a5*T4;
vy = b1 + 2*b2*T + 3*b3*T2 + 4*b4*T3 + 5*b5*T4;
具体来说，
vx = a1 + 2*a2*T + 3*a3*T2 + 4*b4*T3 + 5*a5*T4;
应该是
vx = a1 + 2*a2*T + 3*a3*T2 + 4*a4*T3 + 5*a5*T4;
基于 a 到 x 坐标和 b 到 y 坐标的映射。
这解决了违反约束的问题。
关于convergence/feasibility的问题，我发现确保初始猜测在可行集中（服从等式约束）解决了这个问题；在修复初始条件后，优化器性能的度量（inf_pr 和 inf_du 等...）要小得多。