I have the circuit in this figure:
I get the following error: Initial conditions solve failed to converge. Nonlinear solver: Linear Algebra error. Failed to solve using iteration matrix. The model may not give enough information to make it possible to solve for values of some of its variables. Specific advice is given below.
all components and nodal across variables involved Tie variable x to a definite value, for example by connecting an appropriate domain reference block.
I have looked at the other questions on this on MATLAB answers (did not see any on here) and these solutions did not work:
Changing the S-PS block to filter input.
One question was missing ground but I have that.
Any idea what is wrong?
Hi I had the same problem and I managed to solve mine.
I have a Solve Configuration block in the model.
I was able to run my model initially but I ran into the LA convergence issue when I modified my model.
Once I increased the Nonlinear iterations count the solver managed to complete.
Related
I build a model of solar power plant with the Rankine cycle in Dymola, even though the initialization works fine, but at the time of 2840s, there is a failure to solve the nonlinear equations which leads the simulation to stop.
As shown in the following screenshot, Dymola recommends giving better start values, but it doesn't make any sense, because the failure happens during the simulation instead of initialization. And with better start values, the situation doesn't improve at all.
My question is :
How should I deal with the failure to solve nonlinear equations during simulation?
The error message indicates that the solver was not able to find a solution to a nonlinear system of equations in your model. This could mean either that there is no solution to the system or just that the solver is not able to find it.
The first easy steps would be to try a lower tolerance in simulation settings. And to use another solver that might suit better for your problem. Dassl can handle nonlinear systems quite well. If these steps won't work it might be that there just isn't a solution to your problem and you made a wrong assumption or accidentially have a wrong parameter somewhere.
I am trying to solve a "linearized" linear-system-of-equations, which requires two parameters to be estimated by iteration because of linearization. The actual problem is nonlinear actually, but using fourier series method, it iss linearized.
I have been solving linear system by just matrices and SVDs which takes not much time but these matrices depend on the two parameters that are to be iteratively solved. At the end I just need to make sure that one of the parameters I solve iteratively matches the response I get in the system. This is the criteria to be minimized.
I have been using "fmincon" and "multi-start" to solve for two parameters and I get some results, but it is taking longer than what I expect. There is local minima issue too, so I had to include "multi-start".
Anyone has an idea if any other method would be easier to solve this problem?
I really appreciate it.
A global optimization method that one may use is Simulated annealing.
May be MATLAB has a relevant routine.
There is free Simulated annealing software that you may also try.
I got an improvement in my problem, and I just replied it in comments but I think it is worth putting it in here since what I did emerged something unexpected:
So I ran a monte carlo sim for two variables to be iteratively solved, and plotted how the error changes with respect to input variables. I realized that there are tons of local minima in the error of the response and that's why fmincon was not able to solve itself because it was quickly jumping into one of those local minima holes, and I needed a very refined multi-start for fmincon so that I could get global minimum. This is very interesting observation because I wasn't expecting that rough error distribution with respect to two parameters.
Is there any efficient solver/optimizer in matlab that you know of, to get the global minimum in cases where there are many local minima? Or any other method?
Thanks,
pow=fsolve(#eqns,pop);
This is the code I am using to solve a 2x2 non-linear system of equations, defined in the function eqns.m.
pop is a 2x1 initialisation vector pretty close to the solution. When I run it, the output says
No solution found.fsolve stopped because the relative size of the current step is less than the default value of the step size tolerance squared, but the vector of function values is not near zero as measured by the default value of the function tolerance.<stopping criteria details>
Any way out? I tried moving the initial point further away from the solution intentionally, still it is not working. How do I set the tolerance or some other parameter? Some posts gave me the impression that supplying the jacobian to matlab can be helpful, but how do I do that? Please note that I need the solution in the form of a code which I can put in a function file to be called repeatedly. I believe the interactive optimtool toolbox would not help here. Any help please?
Also from the documentation, the fsolve can employ three different algorithms. Is any of them more helpful than the others for certain problem structures? Where can I get a comparative study of them, suitable for some non-expert in optimisation?
I have been trying to model the Fabry-Perot resonator in simulink. I am not sure if it is right to choose simulink for this task but I have been getting some results, at least. However, I have been also getting an error of algebraic loop when I use a different pair of coupling/reflection parameters. It says,
"Simulink cannot solve the algebraic loop containing
'jblock_multi_MR/Meander2b/Subsystem3/Real-Imag to Complex' at time
6.91999999999991 using the LineSearch-based algorithm due to one of the
following reasons: the model is ill-defined i.e., the system equations do
not have a solution; or the nonlinear equation solver failed to converge
due to numerical issues.
To rule out solver convergence as the cause of this error, either
a) switch to TrustRegion-based algorithm using
set_param('jblock_multi_MR','AlgebraicLoopSolver','TrustRegion')
b) reducing the VariableStepDiscrete solver RelTol parameter so that
the solver takes smaller time steps.
If the error persists in spite of the above changes, then the model is
likely ill-defined and requires modification."
Changing the solver does not help. As a note, I implemented the system in terms of electric fields and complex signals naturally.
thanks for any help.
There is no magic solution for solving algebraic loop issues, as these issues tend to be very model-dependent. Here are a few pointers though:
What are algebraic loops in Simulink and how do I solve them?
How can I resolve algebraic loops in my Simulink model in Simulink 6.5 (R2006b)?
Algebraic Loops in the Simulink documentation
See also this answer to a similar question on SO, with some suggestions for breaking the loop.
I am trying to solve a system of non linear equations using fsolve; lets say
F(x;lambda) = 0, where lambda is a vector of parameters, and x the vector I want to solve for.
I am using Matlab's fsolve.
I have 2 values of the parameter lambda, that I want to solve the system for. For the one value of lambda I get a solution, which seems alright.
For the other value of lambda I get a solution again (matlab exits with a flag of 1. However I know this is not an actual solution For example I know that some of the dimensions of x have to be equal to each other, and this is not the case in the solution I get from fsolve.
I have tried both trust-region and the levenberg-marquardt algorithm, and I am not getting any better results. (explicitly enforcing those x's to be the same, still seems to give solutions that are not consistent with what I would be expecting from the properties of the system)
My question is: do the algorithms used by fsolve depend on any kind of stability of the system? Could it be that changing the parameter lambda in the second case I mention above, I make the system unstable, and could that make fsolve having a hard time to solve it correctly?
Thank you, George
fsolve isn't "failing" - as commented by jucestain, it's giving you a local minimum, which is not necessarily a global minimum. This is what it's designed to do.
To improve your chances of obtaining a global minimum you need to either:
Know that your initial guess is good
Run the optimisation several times with a grid of initial guesses, and pick the best result
Add constraints to prevent the solver straying into areas you know to have local minima
Modify your cost function to remove local minima
If you ever come across a non-linear solver that can guarantee a global minimum, do let us know!