Passing data to nlopt in Rcpp? - r

Question

This is a rather simple question, but I haven't been able to quite find the answer on the web yet.
Wishing my latest attempt, here is latest compiler output:
note: candidate function not viable: no known conversion from 'double (unsigned int, const double *, void *, void )' to 'nlopt_func' (aka 'double ()(unsigned int, const double *, double *, void *)') for 2nd argument
From this error I surmise that I am now wrapping or 'type casting' the data argument correctly and also the parameter vector. The discrepency between the third input, the gradient, confuses me. As I am calling a gradient free optimization routine.
Here is a simple linear regression with a constant and a variable:
#include "RcppArmadillo.h"
// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::depends(nloptr)]]
//#include <vector>
#include <nloptrAPI.h>
using namespace arma;
using namespace Rcpp;
typedef struct {
arma::mat data_in;
} *my_func_data;
typedef struct {
double a, b;
} my_theta;
double myfunc(unsigned n, const double *theta, void *grad, void *data){
my_func_data &temp = (my_func_data &) data;
arma::mat data_in = temp->data_in;
my_theta *theta_temp = (my_theta *) theta;
double a = theta_temp->a, b = theta_temp->b;
int Len = arma::size(data_in)[0];
arma::vec Y1 = data_in(span(0, Len-1), 1);
arma::vec Y2 = data_in(span(0, Len-1), 2);
arma::vec res = data_in(span(0, Len-1), 0) - a*Y1 - b*Y2 ;
return sum(res);
}
// [[Rcpp::export]]
void test_nlopt_c() {
arma::mat data_in(10,3);
data_in(span(0,9),0) = arma::regspace(40, 49);
data_in(span(0,9),1) = arma::ones(10);
data_in(span(0,9),2) = arma::regspace(10, 19);
my_func_data &temp = (my_func_data &) data_in;
double lb[2] = { 0, 0,}; /* lower bounds */
nlopt_opt opt;
opt = nlopt_create(NLOPT_LN_NELDERMEAD, 2); /* algorithm and dimensionality */
nlopt_set_lower_bounds(opt, lb);
nlopt_set_min_objective(opt, myfunc, &data_in );
nlopt_set_xtol_rel(opt, 1e-4);
double minf; /* the minimum objective value, upon return */
double x[2] = {0.5, 0.5}; /* some initial guess */
nlopt_result result = nlopt_optimize(opt, x, &minf);
Rcpp::Rcout << "result:" << result;
return;
}

Answer 1

Got it figured out, stupid answer turns out to be correct, just change 'void' to 'double', no clue why. Anyway, the example code needs some improving but it works.
#include "RcppArmadillo.h"
// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::depends(nloptr)]]
//#include <vector>
#include <nloptrAPI.h>
using namespace arma;
using namespace Rcpp;
typedef struct {
arma::mat data_in;
} *my_func_data;
typedef struct {
double a, b;
} my_theta;
double myfunc(unsigned n, const double *theta, double *grad, void *data){
my_func_data &temp = (my_func_data &) data;
arma::mat data_in = temp->data_in;
my_theta *theta_temp = (my_theta *) theta;
double a = theta_temp->a, b = theta_temp->b;
int Len = arma::size(data_in)[0];
arma::vec Y1 = data_in(span(0, Len-1), 1);
arma::vec Y2 = data_in(span(0, Len-1), 2);
arma::vec res = data_in(span(0, Len-1), 0) - a*Y1 - b*Y2 ;
return sum(res);
}
// [[Rcpp::export]]
void test_nlopt_c() {
arma::mat data_in(10,3);
data_in(span(0,9),0) = arma::regspace(40, 49);
data_in(span(0,9),1) = arma::ones(10);
data_in(span(0,9),2) = arma::regspace(10, 19);
my_func_data &temp = (my_func_data &) data_in;
double lb[2] = { 0, 0,}; /* lower bounds */
nlopt_opt opt;
opt = nlopt_create(NLOPT_LN_NELDERMEAD, 2); /* algorithm and dimensionality */
nlopt_set_lower_bounds(opt, lb);
nlopt_set_min_objective(opt, myfunc, &data_in );
nlopt_set_xtol_rel(opt, 1e-4);
double minf; /* the minimum objective value, upon return */
double x[2] = {0.5, 0.5}; /* some initial guess */
nlopt_result result = nlopt_optimize(opt, x, &minf);
Rcpp::Rcout << "result:" << result;
return;
}