I am building an MPC optimization (IMODE = 6) in Python GEKKO with multiple state (SV) and manipulated (MV) variables. Inside my problem, I would like to define ramping constraints of the following kind:

I’m working on a dynamic optimization problem very similar with this one: Model Reduction with Intermediates in GEKKO. I want to find the best temperature profile that maximizes Biodiesel concentration at final time (x4(tf)), and to compare to a polynomial temperature profile.

I’m using gekko to optimize a certain function. When I use a dummy objective function like m.Ojb(0) just to test for feasibility, the solver is able to find a feasible solution. However, when I add a conditional variable b (see Mix Score section below), that is not subject to any constraints, the solver fails to find a solution. Here’s is some fully functioning test code below:

I’m trying to solve an optimization problem in microeconomics with the Gekko library. In the problem, the objective is to maximize utility (u) subject to the limitation of an income R equal to 100. There are two families indexed by index h and two products indexed by index i. The price of product 1 is equal to 1.

I am using the GEKKO optimization package (https://gekko.readthedocs.io/en/latest/index.html) in python. Since my problem sometimes includes integer variables I run with the following options to get optimization for a steady state mixed integer problem (APOPT):