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I am looking for a C++ library, and I am dealing with convex objective and constraint functions.
I am guessing your problem is non-linear. Where i work, we use SNOPT, Ipopt and another proprietary solver (not for sale). We have also tried and heard good things about Knitro.
As long as your problem is convex, all these solvers work well.
They all have their own API, but they all ask for the same information : values, first and second derivatives.
Assuming your problems are nonlinear, you can use free and open-sourced OPT++, available from Sandia Lab. I have used it in one project in C++ and it was easy to use and worked well.
From what I know, the CPLEX solver is the best convex optimization solver. Its the state of the art in LP solvers. Does convex optimization really well. While looking for it, I see that its IBM's software now. You can find it here : http://www-01.ibm.com/software/integration/optimization/cplex/
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I need to solve the heat equation for 2D object using C++. I've found many sources explaining the mathematics behind it. I am using the Crank-Nicholson scheme, but given that I need a 1001 x 1001 discretization, the resulting linear system is huge and memory would blow in case of a "traditional" implementation.
Therefore, I am recurring to methods for solving sparse linear systems. I have been looking online and cannot find any source explaining an algorithm to solve this sparse system efficiently. I've found several packages that do solve it (like csparse, eigen, SciPy under-the-hood implementation), but found no theoretical explanation to really code it myself. For the moment I am trying to do "reverse engineering" on the source code of SuperLU.
What I am looking for is an algorithm that you may know to solve sparse linear systems.
Thanks in advance
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The TensorFlow white paper mentions that Eigen is used. Are there public explanations for how Eigen was chosen, and are they motivation for using Eigen in TensorFlow C++ op kernels?
I think that one of the key feature that drove the use of Eigen in the first place is because Eigen features its own highly optimized matrix product kernels whereas all other competitors have to be linked to some BLAS libraries. Moreover, the code of Eigen's product kernel is C++ with easy access to low-level internal kernels, so it was 'easy' for them to tweak and extend it to match their needs. This way Google has been able to develop the Tensor module with high CPU performance in a pure header-only fashion. The support for CUDA and now OpenCL via SyCL came later, those are not intrinsic features of Eigen that drove the initial choice.
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For a more complex project, I need to compute the approximate, relative distances of objects from two images (from stereo-cameras). Practically what this neat tutorial explains: https://chrisjmccormick.wordpress.com/2014/01/10/stereo-vision-tutorial-part-i/ and with a result like that
Think I shouldn't be reinventing the wheel for this project and since speed is very important (realtime from two videostreams) I'm looking for a native library (preferably in C++ where the whole project is written in) for this task.
Does anyone have a suggestion?
Open source would be greatest but not mandatory.
Huge thanks in advance!
try with LIBELAS library (Library for Efficient Large-scale Stereo Matching).
Best!
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How might one implement data frame in R, Python, and other languages using C++?
In general, data.frame solves a problem which is solved fundamentally differently in C++ (and other languages) – namely via class hierarchies, or, in the simplest case, via a vector of tuples.
Since you haven’t given specifics it’s hard to know what exactly you are after but if it’s ease of computation, Armadillo is a good linear algebra library for C++ (one among many). I haven’t yet found a good statistics framework for C++ – I suggest simply sticking with R for that.
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Can anyone point out a good C++ library that can do 2D numerical integration. It needs to be able to accept a 2D array of known values, and the spacing between the points can be assumed to be constant (for a start).
It is preferable that it have a license which allows modifying the code as needed.
It's actually a C library, but if the GPL licensing terms work for you try:
http://www.gnu.org/software/gsl/
You will want to check out the Monte Carlo integration options outlined here:
http://www.gnu.org/software/gsl/manual/html_node/Monte-Carlo-Integration.html
This Fortran library is easy to link to from C++ and is in public domain:
http://gams.nist.gov/cgi-bin/serve.cgi/Module/CMLIB/ADAPT/2967
It's single precision but it's quite easy to modify the sources (get "full sources" and go through every function) to switch to double precision.
http://itpp.sourceforge.net/current/
Try this. It can do what you ask for and more! And you can modify the code as much as you like.
I've read somewhere that you can extract libraries out of GNU Octave's code and use the C++ code in your own applications. I'm not sure if that's an easy task, but you can give it a try if you have the time.