This is not exactly related to programming.
I would like to solve linear programming problem in MATLAB without using the in-built functions like linprog from toolboxes.
Any suggestions or examples are much appreciated.
I am using CPLEX for MATLAB toolbox wherein I formulate my MILP as a huge matrix and use the function cplexmilp to call the solver. Since the model I am solving is really huge, I intend to set the option of using multiple processors to speed up the solving of the MILP. I went through the manual but I could not find any specific solution for my case. I'd be grateful if anyone could help me find a solution to this issue.
CPLEX Toolbox Function
Thanks Everyone
Now, I would like to practice some problem solving by use RCGA (Real Code Genetic Algorithms) in MATLAB. Could any helper provide me some example project matlab files about that ?
Thank you very much.
I wrote an article a few years ago on an application of genetic algorithms in MATLAB, which comes with example data and MATLAB code. Although the example doesn't use real-coding, it does demonstrate how to apply a custom chromosomal coding, so perhaps it would be a starting point for you that you could adapt.
I’m implementing softmax regression in Octave. Currently I’m using a non-vectorized implementation using following cost function and derivatives.
Source: Softmax Regression
Now I want to implement vectorized version of it in Octave. It seems like bit hard for me to write vectorized versions for these equations. Can somebody help me to implement this ?
Thanks
Upul
This is very similar to an exercise in Andrew Ng's deep learning class, they give some hints
http://ufldl.stanford.edu/wiki/index.php/Exercise:Vectorization
I have been recently trying to use svm for feature classification. While i was doing so, a question came to my mind.
Which would be a better method to use, LIBSVM or svmclassify? What I mean by svmclassify is to use in-built functions in MATLAB such as svmtrain and svmclassify. In that sense, I was interested to know which method would be more accurate and which would be easier to use.
Since MATLAB has already the Bioinformatics toolbox already, why would you use LIBSVM? Aren't the functions like svmtrain and svmclassify already built in.. what additional benefits does LIBSVM bring about?
I would like to hear some of your opinions. Please Pardon me if the question is stupid..
I expect you would get very similar result using each library.
They are both very easy to use. The only big difference is that one comes with the MATLAB Bioinformatics toolbox and the other one you need to obtain from the authors web site and install by hand. If to you this is an issue I would recommend you stick to what is already installed in your computer. If not consider using LIBSVM, as it is a very well tested and well regarded library.
Also, from personal experience on playing with both, libSVM is much faster than MATLAB svm routines for obvious reasons. Last but not the least, libSVM has MATLAB plugins which can be called from MATLAB if you are more comfortable within a MATLAB environment.
I have also the same question, but I think that Libsvm is very useful and very easy in the case of multi-classes classification , but the matlab toolbox is designed for only two classes classification.
In my experience the libsvm performed giving cross validaion results as 45% where matlab code did 90%. So I looked up the explanation of matlab function for svm where they had such options related with perceptrones, I wonder if they are using pure svm or not but will write again in my case matlab was much better. (multiclass svm)