Can I create a new solver from an old one in Z3? In Z3, the standard process to create a solver is like the following
context ctx;
solver sv(ctx);
After a process of inserting assertions and checking, I want to create a new solver, say sv2, that is equivalent to sv. But I cannot find the supporting function or API. Solving is expensive, that is way I do not want to create sv2 from scratch.
Ting Chen
The usual ways of doing that is either by using push/pull or to solve under assumptions (all on the same solver and context), see Soft/Hard constraints in Z3, Z3/SMT: When should I prefer push/pop to reset?. Also, search for those keywords, there are many questions and answers regarding this issue.
Related
How to show the whole LP/MILP problem.
I am using solver(MPSolver::CreateSolver("SCIP")) in c++.
I saw this issue and this reply. However, those are not helpful for SCIP.
Any help?
There are two approaches:
Option A: get the underlying solver, use the scip solver to export the model.
in, details: use the underlying_solver method on the LinearSolver class.
Then cast the returned pointer to SCIP* (see the implementation of this method for the SCIP solver).
Now you have the solver, you can use the solver's API for anything including exporting the model. The only limitation is that you need the solver to have loaded the model, which only happens at solve time.
Option B:
call one the two export methods. This API is also available in non C++ languages.
Let's say we have the following model:
Collector:
model Collector
Real collect_here;
annotation(defaultComponentPrefixes="inner");
end Collector;
and the following model potentially multiple times:
model Calculator
outer Collector collector;
Real calculatedVariable = 2*time;
equation
calculatedVariable = collector.collect_here;
end Calculator;
The code above works if calcModel is present only once in the system to be simulated. If the model exists more than once I get a singular system. This is demonstrated by the Example below. Changing the parameter works either gives a working or failing system.
model Example
parameter Boolean works = true;
inner Collector collector;
Calculator calculator1;
Calculator calculator2 if not works;
end Example;
Using an array inside the collector to pass multiple variables in it doesn't solve it.
Another possible way to solve this is possible by use of connectors, but I only made it work with one calcModel.
Using multiple instances of Calculator does brake the model, as the single variable calculatedVariable will have multiple equations trying to compute its value. Therefore Dymola complains that the system is structurally singular, in this case meaning that there are more equations than variables in the resulting system of equations.
To give a bit more of an insight: Actually checking Collector will fail, as since Modelica 3.0 every component has to be balanced (meaning it has to have as many unknowns as states), which is not the case for Collector as it does have one unknown but no equation. This strongly limits the possible applications for the inner/outer construct as basically every variable has to be computed where it is defined.
In the given example this is compensated in the overall system if exactly one Calculator is used. So this single combination will work. Although this works, it is something that should not be done - for the obvious reason of being very error-prone (and all sub-models should pass the check).
Your question on how to solve this issue actually misses a description of what the issue actually is. There are some cases in my mind that your approach could be useful for:
You want to plot multiple variables from a single point, which would be collector. For this purpose "variable selections" should be the most straight-forward way to go: see Dymola Manual Vol. 1, Section "4.3.11 Matching and variable selections" on how to apply them.
You want to carry out some mathematical operation on that variables. Then it could be useful to have a vectorized input of variable size. This enables an arbitrary number of connections to this input. For an example of this take a look at: Modelica.Blocks.Math.MultiSum
You want to route multiple signals between different models (which is unlikely judging from your description, but still): Then expandable connectors would be a good possibility. To get an impression of what that does take a look at Modelica.Blocks.Examples.BusUsage.
Hope this helps, otherwise please specify more clearly what you actually want to achieve with your code.
I prepared a demonstrative library for such scenario some days ago. You can access it at https://gist.github.com/beutlich/e630b2bf6cdf3efe96e5e9a637124fe1. If you read the documentation on Example2 you can see the link to an article from H. Elmqvis et. al., which is the clue to your problem. That is, you need a connector, and inherited connects from every Calculator to the one Collector.
I need to rewrite the linkage function in matlab. Now, as I examine it, I realized there is a method called linkagemex inside of it. But I simply cannot step into this method to see its code. Can anyone help me out with this strange situastion?
function Z= linkage (Y, method, pdistArg, varargin)
Z=linkagemex(Y,method);
PS. I think I am pretty good at learning, but matlab is not so easy to learn. If you have good references to learn it well, feel free to let me know. Thanks very much for your time and attention.
As #m.s. mentions, you've found a call to a MEX function. MEX functions are implemented as C code that is compiled into a function callable by MATLAB.
As you've found, you can't step into this method (as it is compiled C code, not MATLAB code), and you don't have access to the C source code, as it's not supplied with MATLAB.
Normally, you would be at kind of a dead end here. Fortunately, that's not quite the case with linkagemex. You'll notice on line 240 of linkage.m that it actually does a test to see whether linkagemex is present. If it isn't, it instead calls a local subfunction linkageold.
I think you can assume that linkageold does at least roughly the same thing as linkagemex. You may like to test them out with a few suitable input arguments to see if they give the same results. If so, then you should be able to rewrite linkage using the code from linkageold rather than linkagemex.
I'm going to comment more generally, related to your PS. Over the last few days I've been answering a few of your questions - and you do seem like a fast learner. But it's not really that MATLAB is hard to learn - you should realize that what you're attempting (rewriting the clustering behaviour of phytree) is not an easy thing to do for even a very advanced user.
MathWorks write their stuff in a way that makes it (hopefully) easy to use - but not necessarily in a way that makes it easy for users to extend or modify. Sometimes they do things for performance reasons that make it impossible for you to modify, as you've found with linkagemex. In addition, phytree is implemented using an old style of OO programming that is no longer properly documented, so even if you have the code, it's difficult to work out what it even does, unless you happen to have been working with MATLAB for years and remember how the old style worked.
My advice would be that you might find it easier to just implement your own clustering method from scratch, rather than trying to build on top of phytree. There will be a lot of further headaches for you down the road you're on, and mostly what you'll learn is that phytree is implemented in an obscure old-fashioned way. If you take the opportunity to implement your own from scratch, you could instead be learning how to implement things using more modern OO methods, which would be more useful for you in the future.
Your call though, that's just my thoughts. Happy to continue trying to answer questions when I can, if you choose to continue with the phytree route.
You came across a MEX function, which "are dynamically linked subroutines that the MATLAB interpreter loads and executes". Since these subroutines are natively compiled, you cannot step into them. See also the MATLAB documentation about MEX functions.
I'm looking for a function in Julia to estimate coefficients for an ARMA process.
For example using the Prediction Error Model as pem and armax in Matlab (part of system identification toolbox) do. pem documentation and armax documentation.
I've looked at the following packages, but can't see that they do what I'm looking for:
TimeSeries.jl
TimeModels.jl
One solution is of course to use Matlab.jl and use the Matlab functions, but I was hoping to do it all in Julia.
If there isn't anything right now, does anyone know of if there are any good Julia functions for multidimensional numerical minimisation (like Newton-Raphson), that can be used for implementing a PEM function?
UPDATE: I've just pushed a module to github called RARIMA.jl. This module can be used to estimate, forecast, and simulate ARIMA models (of which ARMA is a special case). Some of the functions are implemented in Julia, others (particularly estimation) call equivalent R functions using the RCall package which you will need to install and verify it works prior to using RARIMA. The package isn't officially registered (yet), so Pkg.add("RARIMA") won't work for now. If you want to use RARIMA, instead try Pkg.clone("https://github.com/colintbowers/RARIMA.jl"). If this fails, you can file an issue on the repository github page, but be sure to check RCall is installed and working before doing this. Cheers, I'll come back and update here if/when the package is officially registered.
ORIGINAL ANSWER: I just had a glance at the source, and TimeModels does not appear to have any functionality for estimating ARIMA models, although does have one function for simulating them. Given time though, I suspect this will be the package that deals with ARIMA modelling. The TimeSeries package is more about building the object type TimeSeries rather than implementing time series models, so I would be surprised if ARIMA modelling is ever merged into that package.
As near as I can tell, at this point if you want a fully functioning ARIMA package you'll need to use Matlab or R. The R one is very good (see the forecast package written by Rob Hyndman - it is very nice) and is probably easier to interface with from Julia than the Matlab option. Of course, the other option is to start it yourself and merge the code with the TimeModels package :-)
In terms of optimization procedures, Julia has a fair few that are written in Julia, and can be found under the JuliaOpt umbrella. The Optim package in particular is quite popular and well developed. However, most of the people I know who are really into this stuff use NLOpt which is a free open source library callable from many languages (including Julia). I have heard nothing but good things about this library from people who tend to work with this stuff 24/7.
I've started learning evolutionary algorithms (GA, PSO, ...) and I want to implement them in Matlab and play with different parameters to get a hold of the algorithms' structures and how they work.
My problem is, I don't have some simple test functions to use. For example, functions with multiple peaks/valleys, one global minimum and multiple local ones, .... Nothing complicated, just some simple mathematical functions with their formulas.
I can try to make some up with putting some sin/cos/exp together, but it'll take time and is really frustrating!
Anybody knows of a resource (site, book, ...) that have these listed?
Here is a set from our very own #Rody Oldenhuis:
Test functions
You might want to try those in the BBOB benchmark set. There is also some nice accompanying literature to this set in form of the corresponding GECCO workshop.
Some of the classic functions were mentioned by AGS already and include Rastrigin, Rosenbrock and Generalized Rosenbrock, Schwefel, Sphere, Griewank, etc.. We have also implemented these and more in HeuristicLab, so if you want to experiment you can also try that (PSO and GA are included also).