I am working on an optimization problem, and facing difficulty setting up two constraints together in Python. Hereunder, I am simplifying my problem by calculation of area and volume. Only length can be changed, other parameters should remain the same. Constraint 1: Maximum area should be 40000m2 Constraint 2: Minimum volume should be 50000m3 Here, I can set values in
Tag: constraints
Pyomo Constraint error: Trivial Boolean (True) instead of a Pyomo object
I implemented a concrete model in pyomo. It is about a Course planning problem. Courses have to be scheduled within a planning horizon. Teachers, periods and stations have to be linked to the course within a time horizon. The data for the concrete model is being read from Excel. I ran the model with different data. Everything worked well and
Scipy Optimise (minimize) not giving correct results
I am trying to do a simple minimisation as below using SciPy optimise, but the expected results are NOT matching the optimiser output: I would expect the final results to be close to “x_expected”… but that is not the case.. any ideas ? Answer SLSQP solver failed to find the optimal value for your problem. Therefore, inequality constraint does not
How to implement a constrained linear fit in Python?
I’m trying to fit a linear model to a set of data, with the constraint that all the residuals (model – data) are positive – in other words, the model should be the “best overestimate”. Without this constraint, linear models can be easily found with numpy’s polyfit as shown below. example1 Is there an efficient way to implement a linear
Inequality Constraint for a PYMC3 Model
I want to define an inequality constraint for a PYMC3 model. I found this post about defining an equality constraint (i.e., a+b1+b2=1) using pm.Potential. Does anyone know how to change that equality constraint into an inequality constraint like 0.9<a+b1+b2<1? Thanks! Answer The post you mention uses pm.math.eq which stands for “equal”. There are also pm.math.lt (lower than) and pm.math.le (lower