armadillo

I am looking for a fast alternative for the R function rowsum in C++ / Rcpp / Eigen or Armadillo. The purpose is to get the sum of elements in a vector a according to a grouping vector b . For example: > a [1] 2 2 2 2 2 2 2 2 2 2 > b [1] 1 1 1 1 1 2 2 2 2 2 > rowsum(a,b) [,1] 1 10 2 10 Writing a simple for loop in Rcpp is very slow, but maybe my code was just inefficient. I tried also to call the function rowsum in Rcpp , however, rowsum is not very fast. Here's my attempt at doing this using Rcpp (first time using the package, so do point out my inefficiencies): library(inline) library(Rcpp)

I have little experience with parallel programming and was wondering if anyone could have a quick glance at a bit of code I've written and see, if there are any obvious ways I can improve the efficiency of the computation. The difficulty arises due to the fact that I have multiple matrix operations of unequal dimensionality that I need to compute, so I'm not sure the most condensed way of coding the computation. Below is my code. Note this code DOES work. The matrices I am working with are of dimension approx 700x700 [see int s below] or 700x30 [int n]. Also, I am using the armadillo library

I am having some trouble compiling this simple c++ code using Rcpp and the RcppArmadillo package. Take the following simple example to multiply each column of a matrix by a numeric scalar: code <- 'arma::mat out = Rcpp::as<arma::mat>(m); for(int i = 0; i < out.n_cols; ++i){ out.col(i) *= v; } return Rcpp::wrap( out );' Trying to compile this using... require( RcppArmadillo ) armMult <- cxxfunction( signature( m = "numeric" , v = "numeric" ), code , plugin = "RcppArmadillo" ) Results in the compile error.... #error: no match for 'operator*=' in 'arma::Mat<eT>::col(arma::uword) [with eT = double

The NLopt objective function looks like this: double myfunc(const std::vector<double> &x, std::vector<double> &grad, void *my_func_data) x is the data being optimized, grad is a vector of gradients, and my_func_data holds additional data. I am interested in supplying Armadillo matrices A and B to void *my_func_data . I fiddled with Armadillo's member functions mat A(5,5); mat B(5,5); double* A_mem = A.memptr(); double* B_mem = B.memptr(); which gives me a pointers to the matrices A and B. I was thinking of defining another pointer to these pointers: double** CombineMat; int* Arow = A.n_rows;

I'm newbie in C++ programming , but I have a task to compute eigenvalues and eigenvectors (standard eigenproblem Ax=lx ) for symmetric matrices (and hermitian)) for very large matrix of size: binomial(L,L/2) where L is about 18-22. Now I'm testing it on machine which has about 7.7 GB ram available, but at final I'll have access to PC with 64GB RAM. I've started with Lapack++ . At the beginning my project assume to solve this problem only for symmetrical real matrices. This library was great. Very quick and small RAM consuming. It has an option to compute eigenvectors and place into input

I'm a mac user and am trying to install and import C++ Armadillo library. Here are the steps I've had so far: 1) I downloaded the Armadillo library from its website. 2) I went over the Readme.txt file in the download file explaining how to install it. 3) I used CMake to make the armadillo download files into binary files. 4) Then by using terminal and the code sudo make install, I installed the binary codes and they generated some "library-like" files: libarmadillo.4.0.2.dylib, libarmadillo.4.dylib, lib armadillo.dylib 5) I then copied all these files into /url/lib directory. 6) Now I have my