David Bernal
David Bernal
You are absolutely right, the success of a global solver to efficiently solve a convex problem is to identify it is convex. On the other hand, even if it knows...
I would say that the order should be: 1. Quadratic forms (naturally explainable as QUBO using a bridge from quadratic cones to the nonlinear version of the problem) 2. Indicator...
I think this is the right way. This reformulation also works for disjunction $[y_i, x \in X_i]$ or equivalently indicator variables $y_i \to x \in X_i$ https://www.gurobi.com/documentation/9.5/refman/py_model_agc_indicator.html
You are reinventing Disjunctive Programming! Let's go!
I agree with you in resulting as an infeasible problem if we are able to detect it soon enough without even passing it to the solver. If the constraint is...
Can't this be closed given the architecture design from the paper? Do we need to still talk about PBO?
@dvnoble thank you for creating the issue. Did this resolve it? Where in the documentation would you have expected this to appear? Although limited by hands, we would be happy...
It looks good to me. I'd be curious to see the tests to see how to interact with the new code.
I would agree with using PySA (it is extremely stable). Another choice is just using our random samplers (but that wouldn't make nice examples).
Hey Tanuj, You can specify the model type to the GAMS writer as follows: ``` import pyomo.environ as pe solver = pe.SolverFactory('gams') solver.options['mtype']= "miqcp" ``` or whichever model type you...