I am writing programs that are based on robots navigating through mazes (would involve stochastic programming).
Since it will involve heavy matrix handling (plus point for MATLAB) and simulating a robot (plus point for Prolog), I am in a dilemma between the choice of MATLAB and Prolog.
Note: I do have MATLAB at my work environment, hence cost is not an issue.
As mentioned previously, I am not sure if you are looking for comparisons between MATLAB and Python or MATLAB and Prolog. I can speak to the former, at least: MATLAB provides fast linear algebraic computation and a great IDE... and that's about it. Python will cost you much fewer headaches (and dollars), and you can manage "heavy matrix handling" nearly as easily if you tack on Numpy in particular, or SciPy in general.
Also, VPython (Visual Python) is a great 3D visualization tool that uses Numpy under the hood. I developed a robot simulator using VPython; you can see screenshots and example code (for simple wall-following maze navigation) that you can check out in a recent blog post.
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Is there any maximum limit for decision variables in scipy linear programming module (minimization) in python? If so, Can it be extended the number of decision variables to 10000? If scipy is limited to number of decision variables, Is there any other software which can be installed in python so that I can proceed with?
The original scipy Simplex LP solver was only for very small problems. The newer scipy Interior Point solver can handle larger problems more reliably. Also make sure to pass on A_eq and/or A_ub as sparse matrices. If you don't do this you may run out of memory.
Having said this, I would be more comfortable with LP solvers that have seen more large, sparse problems than scipy. Most LP solvers have a Python interface.
Finally, larger problems are often (but not always) more complex and it may help to use a modeling tool. This will allow you to express the problem in a more natural way than using matrices. For Python there is PuLP and Pyomo (among others). Some commercial solvers also provide excellent modeling tools.
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.
This may be the wrong place to ask this, but I can't find a better place on the SE network.
I've briefly worked with both Matlab and Ansys, and from what I have learnt/can gather, Matlab is a programming environment that has functions that perform common math, visualization and analysis operations. You primarily write programs in a textual fashion (.m files) or use Simulink to generate flow graphs (model-based development). Ansys on the other hand is primary a simulation environment where quite a lot can be done simply with the GUI (3D models, physics domains, configuration, display settings), and you can add equations at various points in the simulation engine in order to modify the simulation flow.
Whatever I understand is cursory and only serves as an overview. Can anyone give me a suitable real-world comparison between Matlab and Ansys (or any other simulation product such as COMSOL) that would allow us to understand when to use which, and the weaknesses of each system.
I haven't used Ansys, but Ansys is often compared with Comsol, and I've used Comsol and Matlab for years.
Matlab:
Programming language and environment that runs it. Which means it can do anything (that any other programming language can do). What are its highlights, compared to other languages?
Hundreds of built-in functions to work with Matrices. For example, in one project I needed to do simple matrix algebra (add, multiply, scale matrices), and also needed singular value decomposition. SVD is not something you could write in 50 lines of code, so I needed a ready-made library. At the time I used a library for Java, and wrote my own code for representing matrices and doing matrix algebra on them. That's a few hundreds of lines of code. Had I used Matlab, it would have been about ten lines of code, because all of it is there. I would have needed only to type help svd to find out how to use it. However, if you don't need any of that, stay away from Matlab at all costs! There are much better languages that are free.
Great to use as a calculator that is always open on the desktop, and can do back-of-the-envelope style calculations.
Plotting graphs. Many academics recommend Matlab as the tool of choice for producing publication-quality graphics. These can be exported as PDF and imported into Inkscape for further editing. The best thing is that commands for plotting a graph could be put into a script file, and then parts of it can be changed later as needed, which can save a lot of work compared to manually drawing a graph (imagine you wanted to change the axes or symbols used to present the data points).
Personally, I also use it for curve-fitting. It has many toolboxes, one of which is a neat tool that allows me to find equations that model a set of data points.
Comsol:
Specialised tool for solving partial differential equations (PDEs) on complicated domains using the finite element method (FEM). This might sound obscure, but many real-world engineering needs reduce to this. Such things as:
Finding loads, stresses and strains in civil engineering structures with complicated real-world geometry (what happens when there is gusty wind blowing onto a building or bridge?)
How do currents flow in particular conductive objects?
Chemical reactions in various industrial reactors.
What is the power efficiency of a generator (magnet spinning in coil) design?
How to place aircon outlets in a nontrivially-shaped room to achieve both good temperature distribution and good efficiency?
Comsol, as any other FEM tool that can work with arbitrary equations, can do multiphysics, which means, for example, that one could solve for chemistry of a battery, as well as the temperature and pressure, and how that feeds back into the chemical reaction (speeds up or slows down). Compared with a tool where you need to provide the equations, in Comsol, most of the things that would be needed to solve most problems are already there, and just need to be selected and applied to the geometry, which is also built inside Comsol. Also, equations of arbitrary description can be introduced.
The physical descriptions of how these physical substances behave are called PDEs.
Once Comsol has finished solving a problem, the data could be exported for post-processing into Matlab, which has much more versatile tools for manipulating data and making various plots.
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.
I am looking for a library to plot large data sets with a feature set similar to professional plotting tools (e.g. Matlab, Mathematica), but 3D accelareted. In particular I value
can be scripted like the professional tools above (flexible usage)
has a feature at least as big as these
fast (well, that's what the title says)
I can imagine (actually, I have very much in mind) something like this might exist for python - say, NumPy, SciPy. But I am not well-versed with these libraries yet. It would be great if I could convince people to abandon matlab.
Check out Mayavi, a 3d visualization package for Python that wraps around VTK. It's very flexible, has decent documentation, and hardware-accelerated rendering.
MathGL is cross-platform GPL library written in C/C++ which can plot huge data set (including 2- and 3-ranged data). Its list of graphics types is the same or larger than ones in Matlab and in Mathematica. MathGL have its own scripting language (MGL) and have interfaces to Python (including numpy), Fortran, Octave, Forth, Pascal and so on.