You can get the objective function with:

m.options.OBJFCNVAL

The parameters may be defined in one section or in multiple declarations throughout the model. Parameter initialization is performed sequentially, from top to bottom. If a parameter does not have an initial value given, a default value of 0.0 is assigned. Parameters may also be a function of other parameters or variable initial conditions. These initial conditions are processed once as the first step after the model parsing. All parameters have global scope in the model.,The constants may be defined in one section or in multiple declarations throughout the model. Constant initialization is performed sequentially, from top to bottom. If a constant does not have an initial value given, a default value of 0.0 is assigned. Constants may also be a function of other constants. These initial conditions are processed once as the first step after the model parsing. All constants have global scope in the model.,The variables may be defined in one section or in multiple declarations throughout the model. Variable initialization is performed sequentially, from top to bottom. If a variable does not have an initial value given, a default value of 0.0 is assigned. Variables may also be initialized from parameters or variable initial conditions. These initial conditions are processed once as the first step after the model parsing. All variables have global scope in the model.,The traditional method for absolute value (abs) has a point that is not continuously differentiable at an argument value of zero and can cause a gradient-based optimizer to fail to converge:

c = m.Const(3)

p = m.Param(value = [0, 0.1, 0.2])

v = m.Var(2, lb = 0, ub = 10, integer = True)

fv = m.FV(3, lb = 0, ub = 10) fv.STATUS = 1

mv = m.MV(4, lb = 5, ub = 10) mv.STATUS = 1

sv = m.SV() sv.FSTATUS = 1 sv.MEAS = 6

I have a working program in Gekko that anycodings_python outputs,There is additional documentation on the anycodings_gekko objective function value, solver anycodings_gekko iterations, solve time, solver status, anycodings_gekko and other parameters.,All I want to do is save the value for the anycodings_python objective function. Is there a way to do anycodings_python this?,You can get the objective function with:

I have a working program in Gekko that anycodings_python outputs

```
EXIT: Optimal Solution Found.
The solution was found.
The final value of the objective
function is - 17.8543906759043
-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -
Solver: IPOPT(v3 .12)
Solution time: 0.158899999994901 sec
Objective: -17.8543906759043
Successful solution
-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -
```

You can get the objective function with:

m.options.OBJFCNVAL

GEKKO Python is designed for large-scale optimization and accesses solvers of constrained, unconstrained, continuous, and discrete problems. Problems in linear programming, quadratic programming, integer programming, nonlinear optimization, systems of dynamic nonlinear equations, and multi-objective optimization can be solved. The platform can find optimal solutions, perform tradeoff analyses, balance multiple design alternatives, and incorporate optimization methods into external modeling and analysis software. It is free for academic and commercial use under the MIT license. ,There are 18 example problems with GEKKO that are provided below. These examples demonstrate the equation solving, regression, differential equation simulation, nonlinear programming, machine learning, model predictive control, moving horizon estimation, debugging, and other applications. While these applications are designed to be tutorial in nature and very simple, GEKKO references are further application examples of complex and multi-disciplinary systems. ,The Dynamic Optimization Course is graduate level course taught in three modules to introduce concepts in: ,There are many other applications and instructional material posted to this freely available course web-site. The online course is generally offered starting each year in January. There are two course projects that include the advanced temperature control lab (1st project) and a project that is a group choice (2nd project). Below is an example student presentation at the end of the course. It is representative of the modeling, estimation, and control methods that are taught in the dynamic optimization course.

pip install gekko

^ Mittleman, Hans (1 May 2018). "Decision Tree for Optimization Software". Plato. Arizona State University. Retrieved 1 May 2018. Object-oriented python library for mixed-integer and differential-algebraic equations ,^ "Computational Science: Is there a high quality nonlinear programming solver for Python?". SciComp. Retrieved 1 May 2018. ,^ Everton, Colling. "Dynamic Optimization Projects". Petrobras. Petrobras, Statoil, Facebook. Retrieved 1 May 2018. Example Presentation: Everton Colling of Petrobras shares his experience with GEKKO for modeling and nonlinear control of distillation ,^ W. Hock and K. Schittkowski, Test Examples for Nonlinear Programming Codes, Lecture Notes in Economics and Mathematical Systems, Vol. 187, Springer 1981.

pip install gekko

from gekko import GEKKO m = GEKKO() # Initialize gekko # Initialize variables x1 = m.Var(value = 1, lb = 1, ub = 5) x2 = m.Var(value = 5, lb = 1, ub = 5) x3 = m.Var(value = 5, lb = 1, ub = 5) x4 = m.Var(value = 1, lb = 1, ub = 5) # Equations m.Equation(x1 * x2 * x3 * x4 >= 25) m.Equation(x1 ** 2 + x2 ** 2 + x3 ** 2 + x4 ** 2 == 40) m.Obj(x1 * x4 * (x1 + x2 + x3) + x3) # Objective m.solve(disp = False) # Solve print("x1: " + str(x1.value)) print("x2: " + str(x2.value)) print("x3: " + str(x3.value)) print("x4: " + str(x4.value)) print("Objective: " + str(m.options.objfcnval))

```
from gekko
import brain
import numpy as np
b = brain.Brain()
b.input_layer(1)
b.layer(linear = 3)
b.layer(tanh = 3)
b.layer(linear = 3)
b.output_layer(1)
x = np.linspace(-np.pi, 3 * np.pi, 20)
y = 1 - np.cos(x)
b.learn(x, y)
```

```
import matplotlib.pyplot as plt
xp = np.linspace(-2 * np.pi, 4 * np.pi, 100)
yp = b.think(xp)
plt.figure()
plt.plot(x, y, "bo")
plt.plot(xp, yp[0], "r-")
plt.show()
```

from gekko import GEKKO import numpy as np import matplotlib.pyplot as plt m = GEKKO() # initialize gekko nt = 101 m.time = np.linspace(0, 2, nt) # Variables x1 = m.Var(value = 1) x2 = m.Var(value = 0) u = m.Var(value = 0, lb = -1, ub = 1) p = np.zeros(nt) # mark final time point p[-1] = 1.0 final = m.Param(value = p) # Equations m.Equation(x1.dt() == u) m.Equation(x2.dt() == 0.5 * x1 ** 2) m.Obj(x2 * final) # Objective function m.options.IMODE = 6 # optimal control mode m.solve() # solve plt.figure(1) # plot results plt.plot(m.time, x1.value, "k-", label = r "$x_1$") plt.plot(m.time, x2.value, "b-", label = r "$x_2$") plt.plot(m.time, u.value, "r--", label = r "$u$") plt.legend(loc = "best") plt.xlabel("Time") plt.ylabel("Value") plt.show()