I applied the CP-SAT sovler to sovle a MIQP problem, and it found a optimat result successfully. Do I need to further consider the relaxation method for the problem? Does the CP-SAT sovler support relaxation?
You can have a look at the second part of the CPAIOR 2020 master class on Constraint Programming.
Yes, the solver uses a lot of MIP techniques (presolve, relaxation, cuts) including quadratic cuts.
These are all linears, so nothing using cones.
Related
I'm solving an optimization problem in python with OR-Tools / CP-sat solver. I'm using a file that takes some hours to reach optimal solution. Is there any way of seeing in the terminal how the process is going, like the best solution found so far, the elapsed time, etc...? I know that with cplex solver we can see this.
Thank you
First, you need to add log_search_progress:true to the parameters.
Second, a good way to speed solving it to use multiple workers. This is done by using the num_search_workers:XXX parameter. If you have a decent machine, XXX=8 is good. If you have a beefier machine, you can try XXX=12 or 16 (or more).
I'm trying to solve a complex variant of a min-SAT problem. So far in the process I have two subproblems, both giving solution values that need to be considered in the objective function. However, only one of the two problems do I solve with the OR-tools cp_model module. The other is solved by an external algorithm. Now, ideally I would do the following:
cp-solver findes a solution to the first subproblem,
pause the solver,
solve the second subproblem with an external algorithm, taking as argument the solution found by the cp-solver,
feed the result of the external algorithm back to the cp-solver,
cp-solver now considers as the objective value the sum of the solution it itself found to first subproblem and the solution that was found by the external algorithm,
cp-solver goes to the next iteration and repeats steps 1-6 for a new assignment
So my question is: is there a functionality for Google OR-tools that lets me do something like steps 1-6 where the solver runs in cooperation with external algorithms and is fed values accordingly? I'm new to using this module so I'm unaware of what terms I could search for on Google to find what I need. Thanks a lot my friends. Best regards, 30centimeter.
In the cp-sat solver, solve() is stateless and a black box.
The only thing you can do is modify the model and resolve.
I met an error during initialization when using ThermoSysPro library.
It seems like the Turbine5.Pe is larger than Turbine2.Pe, so the result is negative. but I checked my parameters, there shouldn't be such a problem.
Is this because the nonlinear solver couldn't solve the equation in the following picture?
There is not enough information and I would recommend to set Details and/or Nonlinear iterations in Simulation setup>Debug>Nonlinear solver diagnostics to get more information.
The full expression causing the problem is sqrt((Turbine2.Pe^2-Turbine5.Pe^2)/(Turbine2.Cst*Turbine2.proe.T))
Since the two Pe-values have fixed=true it seems unlikely that they are wrong, but it is impossible to see without the complete model.
However, it is also possible that either Cst or proe.T is negative, or computed to a negative value based on other values.
Without a complete model that is impossible to tell.
According to the comparison between ThermoSysPro(Open source library from EDF https://github.com/alex19941215/ThermoSysPro ) and ThermalPower(Commercial library from Modelon https://www.modelon.com/library/thermal-power-library ), there should be some inspiration for people faced with the same situation.
Here is the code form ThermoSysPro library:
Connectors.FluidInlet Ce
Connectors.FluidOutlet Cs
Here is a type code from Thermal Power library:
Interfaces.FlowPort feed(
h_outflow(start=hstartin))
Interfaces.FlowPort drain(
p(start=pstart),
h_outflow(start=hstartout))
From the code, we can see that in the Thermal Power library each connector's attribute is assigned values according to the parameters, but in the ThermoSysPro library, the connector is using default values, probably zero. So that's why the Thermal Power library has better performance in the term of initialization convergence
I am using Gurobi to run a MIQP (Mixed Integer Quadratic Programming) with linear constraints in Matlab. The solver is very slow and I would like your help to understand whether I can do something about it.
These are the lines which I use to launch the problem
clear model;
clear params;
model.A=[Aineq; Aeq];
model.rhs=[bineq; beq];
model.sense=[repmat('<', size(Aineq,1),1); repmat('=', size(Aeq,1),1)];
model.Q=Q;
model.obj=c;
model.vtype=type;
model.lb=total_lb;
model.ub=total_ub;
params.MIPGap=10^(-1);
result=gurobi(model,params);
This is a screenshot of the output in the Matlab window.
Question 1: It is the first time I am trying to run a MIQP and I would like to have your advice to understand what I can do to improve performance. Let me tell what I have tried so far:
I cheated by imposing params.MIPGap=10^(-1). In this way the phase of node exploration is made shorter. What are the cons of doing this?
I have big-M coefficients and I have tied them to the smallest possible values.
I have tried setting params.ScaleFlag=2; params.ObjScale=2 but it makes things slower
I have changed params.method but it does not seem to help (unless you have some specific recommendation)
I have increase params.Threads but it does not seem to help
Question 2 (minor): Why do I get a negative objective in the root simplex log? How can the objective function be negative?
Without having the full model here, there is not much on advise to give. Tight Big-M formulations are important, but you said, you checked them already. Sometimes splitting them up might help, but this is a complex field.
What might give great benefits for some problems is using the Gurobi parameter tuning tool. So try to export your model and feed the tuning tool with it. It automatically tries different of the hundreds of tuning parameters and might give some nice results.
Regarding the question about negative objectives in the simplex logs, I can think of a couple of possible explanations. First, note that the negative objective values occur in the presence of dual infeasibilities in the dual simplex run. In such a case, I'm not sure exactly what the primal objective values correspond to. Second, if you have a MIQP with products of binaries in the objective, Gurobi may convexify the objective in a way that makes it possible for a negative objective to appear in the reformulated model even when the original model must have a nonnegative objective in any feasible solution.
Is Optimica the only reasonable approach or does there exist more straight forward ways ? I have used OMOPtim in earlier versions but it doesn't work reliably and does not provide one shot optimisation.
PyFMI + Assimulo (CVodes) could do sensitivity analysis
OpenModelica has also a method based on IDAS. See the OpenModelica UserGuide.