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.
Related
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.
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.
Does anyone have a pointer to measurements of how fast a behavioral model written in SystemC is compared to the same model written in SystemVerilog? Or at least first hand experience of the relative simulation speed of the two, when modeling the same thing?
To clarify, this is for high level system simulation, where the model is the minimum code to mimic the behavior. It's decidedly not synthesizable. In System Verilog, it would use as many non-synthesizable behavioral constructs as possible, for maximum execution speed. The point is, we're asking about an apples to apples comparison that someone did, where they tried to do both: stay high level (non-synthesizable), and make the code as close to equivalent as possible between the two languages.
It would be great if they stated the SystemVerilog environment used, and what machine each was run on, in order to be able to translate. Hopefully can be useful to a variety of people in choosing approach.
Thanks!
Like with other language benchmarks it is not possible to answer in general, because it depends on multiple factors like modeling style, compiler vendor and version, compilation options.
Also there are tools to convert Verilog to C++ and C++ to Verilog. So you can always match simulation performance by converting from one language to other.
Verilog can be converted to C++/SystemC using Verilator https://www.veripool.org/wiki/verilator
To convert C++/SystemC into Verilog you can use HLS (High-level synthesis) toos.
I'm trying to model Coulomb static friction in a multi-body system based on the equations from this physics.stackexchange.com post. I have written a very primitive Modelica code to test the equations you may see in this GitHub Gist. I have two main issues:
First of all I get this warning:
Translation Warning
[multibody_Coulomb_static_friction: 43:3-47:9]: In component , in relation V1 == V2, == on Real numbers is only allowed inside functions.
what does it mean and how I can fix this?
The simulation takes like ages to finish. why is it? does it have anything to do with this warning? is there anyway I can do the simulation faster?
P.S.1. I tried running the code in Wolfram SystemModeler, but there it doesn't even succeed to run and I got a error which I have reported here in wolfram forum!
P.S.2 I have posted similar questions here and here.
Looks like you're trying to re-invent the wheel. You say that you want to create a multibody model, but you are not using the Multibody library. Use that. And couple that with rotational or translational components that use friction. An example with such components:
Modelica.Mechanics.Translational.Examples.Friction