Difference between revisions of "Category:Gekko"

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GEKKO works on all platforms (Windows, MacOS, Linux, ARM processors) and with Python 2.7 and 3+. By default, the problem is sent to a public server where the solution is computed and returned to Python. There is a Windows and Linux option to solve without an Internet connection. GEKKO is an extension of the [[APMonitor|APMonitor Optimization Suite]] but has integrated the modeling and solution visualization directly within Python. A mathematical model is expressed in terms of variables and equations such as the Hock & Schittkowski Benchmark Problem #71 used to test the performance of nonlinear programming solvers. This particular optimization problem has an objective function <math>\min_{x\in\mathbb R}\; x_1 x_4 (x_1+x_2+x_3)+x_3</math> and subject to the inequality constraint <math>x_1 x_2 x_3 x_4 \ge 25</math> and equality constraint <math>{x_1}^2 + {x_2}^2 + {x_3}^2 + {x_4}^2=40</math>. The four variables must be between a lower bound of 1 and an upper bound of 5. The initial guess values are <math>x_1 = 1, x_2=5, x_3=5, x_4=1</math>. This optimization problem is solved with GEKKO as shown below.
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GEKKO works on all platforms (Windows, MacOS, Linux, ARM processors) and with Python 2.7 and 3+. By default, the problem is sent to a public server where the solution is computed and returned to Python. There is a Windows and Linux option to solve without an Internet connection. GEKKO is an extension of the APMonitor Optimization Suite but has integrated the modeling and solution visualization directly within Python. A mathematical model is expressed in terms of variables and equations such as the Hock & Schittkowski Benchmark Problem #71 used to test the performance of nonlinear programming solvers. This particular optimization problem has an objective function <math>\min_{x\in\mathbb R}\; x_1 x_4 (x_1+x_2+x_3)+x_3</math> and subject to the inequality constraint <math>x_1 x_2 x_3 x_4 \ge 25</math> and equality constraint <math>{x_1}^2 + {x_2}^2 + {x_3}^2 + {x_4}^2=40</math>. The four variables must be between a lower bound of 1 and an upper bound of 5. The initial guess values are <math>x_1 = 1, x_2=5, x_3=5, x_4=1</math>. This optimization problem is solved with GEKKO as shown below.
  
 
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Latest revision as of 20:08, 13 March 2019

The GEKKO Python package solves large-scale mixed-integer and differential algebraic equations with nonlinear programming solvers (IPOPT, APOPT, BPOPT, SNOPT, MINOS). Modes of operation include machine learning, data reconciliation, real-time optimization, dynamic simulation, and nonlinear model predictive control. In addition, the package solves Linear programming (LP), Quadratic programming (QP), Quadratically constrained quadratic program (QCQP), Nonlinear programming (NLP), Mixed integer programming (MIP), and Mixed integer linear programming (MILP). GEKKO is available in Python and installed with pip from PyPI of the Python Software Foundation. 

pip install gekko

GEKKO works on all platforms (Windows, MacOS, Linux, ARM processors) and with Python 2.7 and 3+. By default, the problem is sent to a public server where the solution is computed and returned to Python. There is a Windows and Linux option to solve without an Internet connection. GEKKO is an extension of the APMonitor Optimization Suite but has integrated the modeling and solution visualization directly within Python. A mathematical model is expressed in terms of variables and equations such as the Hock & Schittkowski Benchmark Problem #71 used to test the performance of nonlinear programming solvers. This particular optimization problem has an objective function \min_{x\in\mathbb R}\; x_1 x_4 (x_1+x_2+x_3)+x_3 and subject to the inequality constraint x_1 x_2 x_3 x_4 \ge 25 and equality constraint {x_1}^2 + {x_2}^2 + {x_3}^2 + {x_4}^2=40. The four variables must be between a lower bound of 1 and an upper bound of 5. The initial guess values are x_1 = 1, x_2=5, x_3=5, x_4=1. This optimization problem is solved with GEKKO as shown below. 

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))

External links

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