I’m looking for a good numerical computing library for C programming language.
Similar to LAPACK with good documentation would be very helpful.
Thanks & Regards,
Upul
It's ugly C (machine translation from Fortran), but you might find clapack useful. You might also want to take a look at ATLAS. There are also quite a few implementations of BLAS in C -- enough that I'd have difficulty picking one to recommend.
Check out the GNU scientific library
http://www.gnu.org/s/gsl/
Related
I know both of them can be programmed. Are they programming languages strictly speaking and turing-complete?
Yes.
If you don't like this answer provide accurate and precise definition of programming language. Turing complete I'm OK with.
MATLAB and Mathematica are fourth-generation programming languages.
You might be interested in seeing these answers to a similar question about Matlab and also this explanation by Wolfram about the ways in which Mathematica is a programming language.
It is Wolfram language from now on, not Mathematica language. References here and here and here
So, yes, the Wolfram language is a computer language.
Is it possible to use a MATLAB code on Scilab? Is that what is meant when saying that Scilab is a "clone" from MATLAB?
There is a tool to automatically convert Matlab source to Scilab source, it's called M2SCI. A script parses the Matlab source code and replaces Matlab-specific functions by Scilab ones. See the documentation of the mfile2sci function.
Yes you can use MATLAB code on scilab. See these links for more information:
http://help.scilab.org/docs/5.4.0/fr_FR/section_36184e52ee88ad558380be4e92d3de21.html
http://help.scilab.org/docs/5.4.0/en_US/index.html
I would not bet on it. But if your code is simple enough chances are good.
Problems are:
There is encrypted p-code in Matlab that Scilab will not be able to open.
Matlab usually comes with a number of toolboxes that might not be available to you (i think especially Simulink)
last but not least (i don't know about scilab) there usually are minute differences in how functions are implemented.
There are a number of projects out there trying to replicate/replace MATLAB:
Julia language: which has a relatively similar syntax to MATLAB and offers great performance, but still lacks a lot of toolboxes/libraries, as well as not having a GUI like MATLAB. I think this has the brightest future among all MATLAB alternatives.
Python language and its libraries NumPy and matplotlib: which is the most used alternative. I think at this moment the community is a couple of orders of magnitude even bigger than MATLAB. Python is the de facto standard in machine learning and data science at the moment. But still, the syntax and memory concept is a bit far from what people are used to in the MATLAB ecosystem. There are also no equivalent to SIMULINK, although Spyder and Jupyter projects have come a long way in terms of the development environment.
Octave: is basically a clone of MATLAB to a point they consider any incompatibility as a bug. If you have a long MATLAB code that you don't want to touch, this is the safest bet. But again no alternative for SIMULINK.
SciLab and it's fork ScicoLab are the best alternatives in terms of GUI, having a SIMULINK replica xcos / scicos and a graphical user interface development features. However the community is not as big as Octave and the syntax is not completely compatible. Sadly the Scilab development team has gone through a devastating family crisis leading to the software falling behind.
Honorary mention of Modelica language implementations OpenModelica and jModelica for being a superior alternative to SIMULINK-SimScape. You should know that you can load Modelica scrips also in xcos and scicos. If you want to kno wmore about JModelica you may see this post.
you may check the MATLAB's Alternativeto page to see more Free and Open source alternatives.
I have been using cvx for doing some experiments with my SDPs. Although it is very easy to use, it is quite slow. I cannot run reasonably large instances in reasonable time. Hence would like someone to point to some faster software.
You can find a list of Semidefinite Programming codes on Hans Mittelmann's Decision Tree for Optimization Software. At least some of the codes are implemented in Matlab (like cvx, right?) or have a Matlab interface.
Anyone knows where I cand find an .m (matlab ) file with the Levenberg-Marquardt moditication to the Newton's method to optimize a function?
Thanks
I always start with a search on file exchange. Found a LMF nonlinear solution. It also seems that there is a lsqnonlin function in The optimization toolbox. Of course that costs a small fortune and limits the portability of your code (one of many reasons I use Python these days).
You can try using the MATLAB MEX version of CMPFIT, if that fits your purpose.
Try it here: http://people.cas.uab.edu/~mosya/cl/MATLABcircle.html
This is a web-page from proffesor Chernov, who published some papers and a book on the matter. There are also c and matlab sources.
I'm doing some numerical estimation and correction with the Kalman filter, and would like to better estimate my parameters of Q and R, preferably dynamically.
http://en.wikipedia.org/wiki/Kalman_filter#Estimation_of_the_noise_covariances_Qk_and_Rk
That article mentions that GNU Octave is currently the best way of determining these parameters from data:
http://en.wikipedia.org/wiki/GNU_Octave#C.2B.2B_integration
Unfortunately it is written for Matlab, and there's supposedly a C++ implementation. I'm very weak in C++ and would not even know how to import a C++ library and link it properly in XCode. All of my C++ libraries to date have been wrapped in 3rd party Objective-C classes.
Has anyone used the C++ implementation for scientific computing or engineering applications on iPhone? I'd appreciate any pointers or tutorials on how to do this kind of analysis with Objective-C.
Additional keywords:
estimating covariance from data
Autocovariance Least-Squares (ALS) technique
noise covariance
Thank you!
I do not know of any such C++ library, if you fancy doing numerical analysis on iOS, the best way to go is the accelerate framework, specifically (from this description):
Linear Algebra: LAPACK and BLAS
The Basic Linear Algebra Subprograms (BLAS) and Linear Algebra Package
(LAPACK) libraries contain—as you would expect—functions to perform
linear algebra computations such as solving simultaneous linear
equations, least squares solutions of linear equations, and eigenvalue
problems. The BLAS library serves as a building block for the LAPACK
library. The BLAS and LAPACK libraries are widely distributed and
industry standard computational libraries. They are available on a
number of different platforms and architectures. So, if you are
already using these libraries you should feel right at home, as the
APIs are exactly the same on Mac OS X.
You'll need a fairly good grounding in C, pointers, arrays and such though, no way around it I feel. There is a detailed description of how to use these linear algebra primitives to implement kalman filtering (although this is using R, so probably not of mush use to you).
This is a SO post on Kalman Filtering which expressed my opinion quite well. I'm afraid I think the chances of finding a magic Objective-C wrapper for Kalman Filtering are fairly low, though I would be very happy to be proven wrong!