gekko

问题 Sometimes my Python Gekko application is solved better with one solver over another. It is difficult to predict which solver will perform best. Because Python Gekko supports local or remote solves on different servers with m.GEKKO(server='http://{address}') , is it possible to create a parallel Gekko application that will try all the solvers simultaneously on any number of computers (including local) and then kill the other processes when the first one returns successfully? I've been looking

问题 Sometimes my Python Gekko application is solved better with one solver over another. It is difficult to predict which solver will perform best. Because Python Gekko supports local or remote solves on different servers with m.GEKKO(server='http://{address}') , is it possible to create a parallel Gekko application that will try all the solvers simultaneously on any number of computers (including local) and then kill the other processes when the first one returns successfully? I've been looking

问题 In a modeling framework for solving systems of differential equations with GEKKO that I am writing I want to calculate the maximum of a parameter (an external forcing) and an integer value (e.g. 0) and use the result in the model equations. The crucial problem is that for passing the parameter through vectorized functions, the parameter currently is wrapped as an Intermediate equation. This is why a vectorized maximum function, such as built-in max and np.maximum return TypeError: object of

问题 In a modeling framework for solving systems of differential equations with GEKKO that I am writing I want to calculate the maximum of a parameter (an external forcing) and an integer value (e.g. 0) and use the result in the model equations. The crucial problem is that for passing the parameter through vectorized functions, the parameter currently is wrapped as an Intermediate equation. This is why a vectorized maximum function, such as built-in max and np.maximum return TypeError: object of

问题 In a modeling framework for solving systems of differential equations with GEKKO that I am writing I want to calculate the maximum of a parameter (an external forcing) and an integer value (e.g. 0) and use the result in the model equations. The crucial problem is that for passing the parameter through vectorized functions, the parameter currently is wrapped as an Intermediate equation. This is why a vectorized maximum function, such as built-in max and np.maximum return TypeError: object of

问题 I tried to convert an example from gekko python optimizer by using the list, array x[] instead of variables x1 .. x4 . This is the code which gives the result, but I think it is not correct from gekko import GEKKO import numpy as np # Initialize Model m = GEKKO(remote=False) #help(m) #define parameter eq = m.Param(value=40) #initialize variables x = [m.Var(value=1,lb=1,ub=5) for i in range(4)] x[1].value=5 x[2].value=5 #Equations m.Equation(np.prod([x[i] for i in range(0,4)])>=25) m.Equation

来源： https://stackoverflow.com/questions/64542594/how-could-constraints-be-dynamically-constructed-in-gekko

来源： https://stackoverflow.com/questions/63991323/gekko-pred-hor-and-ctrl-hor-vs-m-time

来源： https://stackoverflow.com/questions/64040582/the-control-horizon-and-prediction-horizon

来源： https://stackoverflow.com/questions/55436296/gekko-non-linear-optimization-object-type-error-in-constraint-function-evaluati