I wrote a model in Ampl and I wanted to solve it by glpk. But I noticed that I need some cplex's operation such that ==>, and glpk does not have it. I am wondering is there an easy way to convert Ampl file to cplex? or it is a difficult process and I have to rewrite every thing in Cplex format!
(I did not buy ampl so I can not use cplex in ampl)
If you do not have the AMPL software, then you cannot run AMPL files and convert them automatically to any form.
The GLPK package does have its own modeling language, GNU Mathprog, which implements a linear subset of AMPL. Thus sometimes it is not so hard to translate an AMPL model to a GNU Mathprog model. Then GLPK can process the GNU Mathprog model and produce an MPS or LP file that can be read by a solver.
GNU Mathprog does not have the ==> ("implies") operator found in AMPL, however, so any AMPL constraints using that operator will have to be translated into linear constraints. There are well-known ways to make such a translation. If you need help with doing it, then that should be posted as a separate question. (You will have the best chance of getting an answer if you show the complete AMPL constraint that you are trying to translate.)
Related
I have a small question; I am solving MIP Model , coded on C++ and solving by Cplex solver. I remember that when I test the model with relatively smaller instances , it was giving me "infeasibility row …."; Now ,I test the same model on a large size instance and I get the infeasibility and it does not tell me which row causes infeasibility. How can I find the which parameter or constraint causes infeasibility ? While the larger instance is tested, the presolve is performed, may it cause the infeasibility? I googled about conflict refiner but could not find a small and clear example explaining how to invoke it ? I will be very happy, if you have any suggestions or ideas
Thank you
Another way to find where the infeasibility comes is to export your model as an LP file or similar, then try to solve it with the standalone cplex. It helps if you name your variables and constraints sensibly. Then you have all the interactive tools in cplex to help you find where the issues are.
in C++ you should have a look at FeasOpt
In the documentation see
CPLEX > User's Manual for CPLEX > Infeasibility and unboundedness
If you model in OPL you could call the relaxation from concert C++ APIs
Using Microsoft Solver Foundation,I am trying to solve a linear program of the form Ax <= b where A is a matrix containing thousands of data points.
I know that I can new up a Model object and then use the AddConstraint method to add constraints in equation form. However putting those equations together where each contains thousands of variables is just not possible. I looked at the Model Class and can not find a way to just give it the matrix and other info.
How can I do this?
Thanks!
You can make A a parameter and bind data to it. Warning: Microsoft Solver Foundation has been discontinued a while ago, so you are advised to consider an alternative modeling system.
I'd have thought that google could answer this question, but I've not had much luck.
Does anyone know of any open source C++ implementations of any face detection algorithms other than the Viola-Jones (boosted cascades of Haar-like features) method?
Also, does there exist an open source C++ implementation of Fisherfaces anywhere?
Thanks.
This post gets some attention, so I'd like to update it. I've contributed the face recognition library I wrote to OpenCV, which includes Eigenfaces, Fisherfaces and Local Binary Patterns Histograms at time of writing this. So OpenCV 2.4.2 now comes with everything to get started, see the very detailed documentation:
http://docs.opencv.org/trunk/modules/contrib/doc/facerec/
Now the original answer.
I am the author of the article linked in Kevin's post. Please note that you need to find the eigenvalues of the non-symmetric matrix S_{W}^{-1} S_{B} for the Fisherfaces, I didn't explicitly mention it in my blog. OpenCV only has a solver for symmetric matrices in its current version; since eigenvalues and singular values aren't equivalent for non-symmetric matrices you can't use a SVD either. For my project I have adapted the JAMA solver to C++ for solving the eigenvalue problem for non-symmetric matrices, so there's no need to use an external library for it. The CMakeLists.txt is configured, so Eigen can be used as well, so you have the choice.
Now I've finally found some minutes to implement the Fisherfaces method with the OpenCV2 C++ API and pushed the code into my github account at:
https://github.com/bytefish/opencv/blob/master/lda
The main.cpp shows you how to use the Fisherfaces class and how to use the Linear Discriminant Analysis with the same example as on: http://www.bytefish.de/wiki/pca_lda_with_gnu_octave. It comes as a CMake project, so compiling is as easy as typing:
philipp#mango:~/some/dir$ mkdir build; cd build
philipp#mango:~/some/dir/build$ cmake ..
philipp#mango:~/some/dir/build$ make
philipp#mango:~/some/dir/build$ ./lda
I don't know if it's the preferred Stackoverflow way to post code in the answer, but I think it's a bit too long to post.
Please note two things. (1) I read the images from a CSV file (just like this one), you don't have to care about the order of the labels. (2) I store the eigenvectors by column, while the PCA in OpenCV stores them by row. It's just a matter of personal taste to do so, but I've never seen that for any other solver and so I decided to store them by column.
I'd have thought that google could answer this question, but I've not had much luck.
Does anyone know of any open source C++ implementations of any face detection algorithms other than the Viola-Jones (boosted cascades of Haar-like features) method?
Also, does there exist an open source C++ implementation of Fisherfaces anywhere?
Thanks.
This post gets some attention, so I'd like to update it. I've contributed the face recognition library I wrote to OpenCV, which includes Eigenfaces, Fisherfaces and Local Binary Patterns Histograms at time of writing this. So OpenCV 2.4.2 now comes with everything to get started, see the very detailed documentation:
http://docs.opencv.org/trunk/modules/contrib/doc/facerec/
Now the original answer.
I am the author of the article linked in Kevin's post. Please note that you need to find the eigenvalues of the non-symmetric matrix S_{W}^{-1} S_{B} for the Fisherfaces, I didn't explicitly mention it in my blog. OpenCV only has a solver for symmetric matrices in its current version; since eigenvalues and singular values aren't equivalent for non-symmetric matrices you can't use a SVD either. For my project I have adapted the JAMA solver to C++ for solving the eigenvalue problem for non-symmetric matrices, so there's no need to use an external library for it. The CMakeLists.txt is configured, so Eigen can be used as well, so you have the choice.
Now I've finally found some minutes to implement the Fisherfaces method with the OpenCV2 C++ API and pushed the code into my github account at:
https://github.com/bytefish/opencv/blob/master/lda
The main.cpp shows you how to use the Fisherfaces class and how to use the Linear Discriminant Analysis with the same example as on: http://www.bytefish.de/wiki/pca_lda_with_gnu_octave. It comes as a CMake project, so compiling is as easy as typing:
philipp#mango:~/some/dir$ mkdir build; cd build
philipp#mango:~/some/dir/build$ cmake ..
philipp#mango:~/some/dir/build$ make
philipp#mango:~/some/dir/build$ ./lda
I don't know if it's the preferred Stackoverflow way to post code in the answer, but I think it's a bit too long to post.
Please note two things. (1) I read the images from a CSV file (just like this one), you don't have to care about the order of the labels. (2) I store the eigenvectors by column, while the PCA in OpenCV stores them by row. It's just a matter of personal taste to do so, but I've never seen that for any other solver and so I decided to store them by column.
I'm using libsvm library in my project and have recently discovered that it provides out-of-the-box cross validation.
I'm checking the documentation and it says clearly that I have to call svm-train with -n switch to use CV feature
.
When I call it with -v switch I cannot get a model file which is needed by svm-predict.
Implementing Support Vector Machine from scratch is beyond the scope of my project, so I'd rather fix this one if it is broken or ask the community for support.
Can anybody help with that?
Here's the link to the library, implemented in C and C++, and here is the paper that describes how to use it.
Cause libsvm use cv only for parameter selection.
From libsvm FAQ:
Q: After doing cross validation, why there is no model file outputted ?
Cross validation is used for selecting good parameters. After finding them, you want to re-train the whole data without the -v option.
If you are going to use cv for estimating quality of classifier on your data you should implement external cross validation by splitting data, train on some part and test on other.
It's been a while since I used libsvm so I don't think I have the answer you're looking, but if you run the cross-validation and are satisfied with the results, running lib-svm with the same parameters without the -v will yield the same model.