eigen | linear algebra : matrices , vectors , numerical solvers | Math library

 by   PX4 C++ Version: Current License: Non-SPDX

kandi X-RAY | eigen Summary

kandi X-RAY | eigen Summary

eigen is a C++ library typically used in Utilities, Math applications. eigen has no bugs, it has no vulnerabilities and it has low support. However eigen has a Non-SPDX License. You can download it from GitHub.

For more information go to

            kandi-support Support

              eigen has a low active ecosystem.
              It has 323 star(s) with 99 fork(s). There are 75 watchers for this library.
              It had no major release in the last 6 months.
              There are 0 open issues and 3 have been closed. On average issues are closed in 20 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of eigen is current.

            kandi-Quality Quality

              eigen has 0 bugs and 0 code smells.

            kandi-Security Security

              eigen has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              eigen code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              eigen has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              eigen releases are not available. You will need to build from source code and install.
              It has 566 lines of code, 27 functions and 10 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of eigen
            Get all kandi verified functions for this library.

            eigen Key Features

            No Key Features are available at this moment for eigen.

            eigen Examples and Code Snippets

            No Code Snippets are available at this moment for eigen.

            Community Discussions


            Can OpenMP's SIMD directive vectorize indexing operations?
            Asked 2022-Feb-13 at 21:08

            Say I have an MxN matrix (SIG) and a list of Nx1 fractional indices (idxt). Each fractional index in idxt uniquely corresponds to the same position column in SIG. I would like to index to the appropriate value in SIG using the indices stored in idxt, take that value and save it in another Nx1 vector. Since the indices in idxt are fractional, I need to interpolate in SIG. Here is an implementation that uses linear interpolation:



            Answered 2022-Jan-19 at 18:55

            Theoretically, it should be possible, assuming the processor support this operation. However, in practice, this is not the case for many reasons.

            First of all, mainstream x86-64 processors supporting the instruction set AVX-2 (or AVX-512) does have instructions for that: gather SIMD instructions. Unfortunately, the instruction set is quite limited: you can only fetch 32-bit/64-bit values from the memory base on 32-bit/64-bit indices. Moreover, this instruction is not very efficiently implemented on mainstream processors yet. Indeed, it fetch every item separately which is not faster than a scalar code, but this can still be useful if the rest of the code is vectorized since reading many scalar value to fill a SIMD register manually tends to be a bit less efficient (although it was surprisingly faster on old processors due to a quite inefficient early implementation of gather instructions). Note that is the SIG matrix is big, then cache misses will significantly slow down the code.

            Additionally, AVX-2 is not enabled by default on mainstream processors because not all x86-64 processors supports it. Thus, you need to enable AVX-2 (eg. using -mavx2) so compilers could vectorize the loop efficiently. Unfortunately, this is not enough. Indeed, most compilers currently fail to automatically detect when this instruction can/should be used. Even if they could, then the fact that IEEE-754 floating point number operations are not associative and values can be infinity or NaN generally does not help them to generate an efficient code (although it should be fine here). Note that you can tell to your compiler that operations can be assumed associated and you use only finite/basic real numbers (eg. using -ffast-math, which can be unsafe). The same thing apply for Eigen type/operators if compilers fail to completely inline all the functions (which is the case for ICC).

            To speed up the code, you can try to change the type of the SIG variable to a matrix reference containing int32_t items. Another possible optimization is to split the loop in small fixed-size chunks (eg.32 items) and split the loop in many parts so to compute the indirection in a separate loops so compilers can vectorize at least some of the loops. Some compilers likes Clang are able to do that automatically for you: they generate a fast SIMD implementation for a part of the loop and do the indirections use scalar instructions. If this is not enough (which appear to be the case so far), then you certainly need to vectorize the loop yourself using SIMD intrinsics (or possible use SIMD libraries that does that for you).

            Source https://stackoverflow.com/questions/70775446


            How to Add Title To SEM Plot in R
            Asked 2022-Feb-06 at 11:43

            This is what I have for the plot:



            Answered 2022-Feb-06 at 11:27

            Finally, I put this to the side for some time when I got more R savvy. Instead of trying to overcomplicate things, I decided to make a really simple SEM path plot, then apply what was said in the comments here earlier to solve the issue.


            So the major issue I kept having was getting the title to map on. For some reason I couldn't understand what was causing the issue...until I figured out the order of operations for printing out the plot. So here is basically what I did. First I used a well-oiled data frame and wrote a model based off the old lavaan manual:

            Source https://stackoverflow.com/questions/68779872


            Most efficient way to return Eigen::VectorXi with more than 2^31-1 elements to R
            Asked 2022-Jan-16 at 16:36

            I have a vector x of type Eigen::VectorXi with more than 2^31-1 entries, which I would like to return to R. I can do that by copying x entry-wisely to a new vector of type Rcpp::IntegerVector, but that seems to be quite slow.

            I am wondering:

            1. whether there is a more efficient workaround;
            2. why in the following reproducible example Rcpp::wrap(x) doesn't work.




            Answered 2022-Jan-16 at 16:36

            Rcpp::wrap is dispatching to a method for Eigen matrices and vectors implemented in RcppEigen. That method doesn't appear to support long vectors, currently. (Edit: It now does; see below.)

            The error about negative length is thrown by allocVector3 here. It arises when allocVector3 is called with a negative value for its argument length. My guess is that Rcpp::wrap tries to represent 2^31 as an int, resulting in integer overflow. Maybe this happens here?

            In any case, you seem to have stumbled on a bug, so you might consider sharing your example with the RcppEigen maintainers on GitHub. (Edit: Never mind - I've just submitted a patch.) (Edit: Patched now, if you'd like to build RcppEigen from sources [commit 5fd125e or later] in order to update your Rcpp::wrap.)

            Attempting to answer your first question, I compared your two approaches with my own based on std::memcpy. The std::memcpy approach supports long vectors and is only slightly slower than Rcpp::wrap.

            The std::memcpy approach

            The C arrays beneath Eigen::VectorXi x and Rcpp::IntegerVector y have the same type (int) and length (n), so they contain the same number of bytes. You can use std::memcpy to copy that number of bytes from one's memory address to other's without a for loop. The hard part is knowing how to obtain the addresses. Eigen::VectorXi has a member function data that returns the address of the underlying int array. R objects of integer type use INTEGER from the R API, which does the same thing.


            Source https://stackoverflow.com/questions/70714510


            Is std::ranges::size supposed to return an unsigned integer?
            Asked 2021-Dec-27 at 09:10

            Here it is written that std::ranges::size should return an unsigned integer. However, when I use it on an Eigen vector (with Eigen 3.4) the following compiles:



            Answered 2021-Dec-22 at 19:15

            Is this a bug of std::ranges::size?

            No. The cppreference documentation is misleading. There is no requirement for std::ranges::size to return an unsigned integer. In this case, it returns exactly what Eigen::VectorXd::size returns.

            For ranges that model ranges::sized_range, that would be an unsigned integer, but Eigen::VectorXd evidently does not model such range.

            But then what is the purpose of std::ranges::ssize compared to std::ranges::size?

            The purpose of std::ranges::ssize is to be a generic way to get a signed value regardless of whether std::ranges::size returns signed or unsigned. There is no difference between them in cases where std::ranges::size returns a signed type.

            Is there a reference to back up what you state?

            Yes. See the C++ standard:


            Otherwise, if disable_­sized_­range> ([range.sized]) is false and auto(t.size()) is a valid expression of integer-like type ([iterator.concept.winc]), ranges​::​size(E) is expression-equivalent to auto(​t.size()).

            Source https://stackoverflow.com/questions/70453599


            Vectors do not satisfy std::ranges::contiguous_range in Eigen 3.4
            Asked 2021-Dec-22 at 00:28

            Why does Eigen::VectorXd not satisfy the concept std::ranges::contiguous_range? That is, static_assert(std::ranges::contiguous_range); does not compile.

            Also, is there the possibility to specialize a template to make Eigen vectors satisfy the contiguous range concept? For instance, we can specialize std::ranges::enable_borrowed_range to make any range satisfy the std::range::borrowed_range concept. In other words, is there a way to make the above static assertion compile?



            Answered 2021-Dec-21 at 22:48

            Contiguous ranges have to be opted into. There is no way to determine just by looking at an iterator whether or not it is contiguous or just random access. Consider the difference between deque::iterator and vector::iterator - they provide all the same operations that return all the same things, how would you know unless the vector::iterator explicitly told you?

            Eigen's iterators do not do this yet. Indeed, before C++20 there was no notion of a contiguous iterator to begin with. That's new with C++20 Ranges.

            You can see this if you try to just verify that it is contiguous:

            Source https://stackoverflow.com/questions/70442139


            Returned Eigen Matrix from templated function changes value
            Asked 2021-Dec-14 at 15:27

            I came around some weird behavior concerning the eigen library and templated functions.

            Maybe someone can explain to me, why the first version is not working, while the other 3 do. My guess would be the first case freeing up some local variable, but hopefully someone can enlighten me. Thanks in advance.

            Here is the code:

            Compiler-Explorer: https://compiler-explorer.com/z/r45xzE417



            Answered 2021-Dec-14 at 15:27


            How to map a list of Numpy matrices to a vector of Eigen matrices in Cython
            Asked 2021-Dec-01 at 18:34

            I have a C++ function which I want to run from Python. For this I use Cython. My C++ function relies heavily on Eigen matrices which I map to Python's Numpy matrices using Eigency.

            I cannot get this to work for the case where I have a list of Numpy matrices.

            What does works (mapping a plain Numpy matrix to an Eigen matrix):

            I have a C++ function which in the header (Header.h) looks like:



            Answered 2021-Dec-01 at 18:34

            Thanks to @ead I found a solution.

            FlattenedMapWithOrder has implementation so it can be assinged to an Eigen::Matrix. However, std::vector does not have such functionality and since std::vector and std::vector are of a different type, they cannot be assigned to one another. More about this here. The implementation in FlattenedMapWithOrder mentioned above is here.

            To solve this, the function in the C++ code called from Cython need to simply have as input argument the matching type: std::vector. To do this, the C++ code needs to know the definition of type FlattenedMapWithOrder.

            To do this, you need to #include "eigency_cpp.h". Unfortunately, this header is not self contained. Therefore, (credits to @ead) I added these lines:

            Source https://stackoverflow.com/questions/69584633


            How to enable and disable Intel MKL in numpy Python?
            Asked 2021-Nov-25 at 12:30

            I want to test and compare Numpy matrix multiplication and Eigen decomposition performance with Intel MKL and without Intel MKL.

            I have installed MKL using pip install mkl (Windows 10 (64-bit), Python 3.8).

            I then used examples from here for matmul and eigen decompositions.

            How do I now enable and disable MKL in order to check numpy performance with MKL and without it?

            Reference code:



            Answered 2021-Nov-25 at 12:30

            You can use different environments for the comparison of Numpy with and without MKL. In each environment you can install the needed packages(numpy with MKL or without) using package installer. Then on that environments you can run your program to compare the performance of Numpy with and without MKL.

            NumPy doesn’t depend on any other Python packages, however, it does depend on an accelerated linear algebra library - typically Intel MKL or OpenBLAS.

            • The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS.

            • In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users' environment when they install NumPy.

            • When a user installs NumPy from conda-forge, that BLAS package then gets installed together with the actual library.But it can also be MKL (from the defaults channel), or even BLIS or reference BLAS.

            Please refer this link to know about installing Numpy in detail.

            You can create two different environments to compare the NumPy performance with MKL and without it. In the first environment install the stand-alone NumPy (that is, the NumPy without MKL) and in the second environment install the one with MKL.

            To create environment using NumPy without MKL.

            Source https://stackoverflow.com/questions/69986869


            Use CppADCodeGen with CMake FetchContent or ExternalProject
            Asked 2021-Nov-25 at 10:56

            I am not good with CMake, and I cannot find good explanations about how to use its FetchContent functionality. Indeed, most repositories seem to require different treatment, and the rules of such treatment defy my comprehension.

            That said, here is my problem. I would like to use CppADCodeGen in my project using CMake FetchContent. Here is my code:



            Answered 2021-Oct-26 at 20:48
            Problems Overview

            As seen in the output you've provided, there are 2 problems:

            1. There is a target name conflict between probably CppAD and eigen. They both have the uninstall target. It can be seen here:

            Source https://stackoverflow.com/questions/69686099


            Speed-up eigen c++ transpose?
            Asked 2021-Nov-21 at 11:50

            I know that this 'eigen speed-up' questions arise regularly but after reading many of them and trying several flags I cannot get a better time with c++ eigen comparing with the traditional way of performing a transpose. Actually using blocking is much more efficient. The following is the code



            Answered 2021-Nov-21 at 11:50

            As suggested by INS in the comment is the actual copying of the matrix causing the performance drop, I slightly modify your example to use some numbers instead of all zeros (to avoid any type of optimisation):

            Source https://stackoverflow.com/questions/70052298

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install eigen

            You can download it from GitHub.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
          • HTTPS


          • CLI

            gh repo clone PX4/eigen

          • sshUrl


          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link