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我有大規模的QP問題，所以我在R中使用Gurobi優化器。但是，我希望使用廣義順序最小優化算法，但我無法在R軟件包中找到它。所以我試圖調用這個C函數，但失敗了。從R調用這個C函數（libqp_gsmo.c）的方法是什麼？
如何從R調用這個C函數？
#include <math.h>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <stdint.h>
#include <limits.h>
#include "libqp.h"
libqp_state_T libqp_gsmo_solver(const double* (*get_col)(uint32_t),
double *diag_H,
double *f,
double *a,
double b,
double *LB,
double *UB,
double *x,
uint32_t n,
uint32_t MaxIter,
double TolKKT,
void (*print_state)(libqp_state_T state))
{
double *col_u;
double *col_v;
double *Nabla;
double minF_up;
double maxF_low;
double tau;
double F_i;
double tau_ub, tau_lb;
double Q_P;
uint32_t i, j;
uint32_t u, v;
libqp_state_T state;
Nabla = NULL;
/* ------------------------------------------------------------ */
/* Initialization */
/* ------------------------------------------------------------ */
/* Nabla = H*x + f is gradient*/
Nabla = (double*)LIBQP_CALLOC(n, sizeof(double));
if(Nabla == NULL)
{
state.exitflag=-1;
goto cleanup;
}
/* compute gradient */
for(i=0; i < n; i++)
{
Nabla[i] += f[i];
if(x[i] != 0) {
col_u = (double*)get_col(i);
for(j=0; j < n; j++) {
Nabla[j] += col_u[j]*x[i];
}
}
}
if(print_state != NULL)
{
state.QP = 0;
for(i = 0; i < n; i++)
state.QP += 0.5*(x[i]*Nabla[i]+x[i]*f[i]);
print_state(state);
}
/* ------------------------------------------------------------ */
/* Main optimization loop */
/* ------------------------------------------------------------ */
state.nIter = 0;
state.exitflag = 100;
while(state.exitflag == 100)
{
state.nIter ++;
/* find the most violating pair of variables */
minF_up = LIBQP_PLUS_INF;
maxF_low = -LIBQP_PLUS_INF;
for(i = 0; i < n; i++)
{
F_i = Nabla[i]/a[i];
if(LB[i] < x[i] && x[i] < UB[i])
{ /* i is from I_0 */
if(minF_up > F_i) { minF_up = F_i; u = i; }
if(maxF_low < F_i) { maxF_low = F_i; v = i; }
}
else if((a[i] > 0 && x[i] == LB[i]) || (a[i] < 0 && x[i] == UB[i]))
{ /* i is from I_1 or I_2 */
if(minF_up > F_i) { minF_up = F_i; u = i; }
}
else if((a[i] > 0 && x[i] == UB[i]) || (a[i] < 0 && x[i] == LB[i]))
{ /* i is from I_3 or I_4 */
if(maxF_low < F_i) { maxF_low = F_i; v = i; }
}
}
/* check KKT conditions */
if(maxF_low - minF_up <= TolKKT)
state.exitflag = 4;
else
{
/* SMO update of the most violating pair */
col_u = (double*)get_col(u);
col_v = (double*)get_col(v);
if(a[u] > 0)
{ tau_lb = (LB[u]-x[u])*a[u]; tau_ub = (UB[u]-x[u])*a[u]; }
else
{ tau_ub = (LB[u]-x[u])*a[u]; tau_lb = (UB[u]-x[u])*a[u]; }
if(a[v] > 0)
{ tau_lb = LIBQP_MAX(tau_lb,(x[v]-UB[v])*a[v]); tau_ub =
LIBQP_MIN(tau_ub,(x[v]-LB[v])*a[v]); }
else
{ tau_lb = LIBQP_MAX(tau_lb,(x[v]-LB[v])*a[v]); tau_ub =
LIBQP_MIN(tau_ub,(x[v]-UB[v])*a[v]); }
tau = (Nabla[v]/a[v]-Nabla[u]/a[u])/
(diag_H[u]/(a[u]*a[u]) + diag_H[v]/(a[v]*a[v]) -
2*col_u[v]/(a[u]*a[v]));
tau = LIBQP_MIN(LIBQP_MAX(tau,tau_lb),tau_ub);
x[u] += tau/a[u];
x[v] -= tau/a[v];
/* update Nabla */
for(i = 0; i < n; i++)
Nabla[i] += col_u[i]*tau/a[u] - col_v[i]*tau/a[v];
}
if(state.nIter >= MaxIter)
state.exitflag = 0;
if(print_state != NULL)
{
state.QP = 0;
for(i = 0; i < n; i++)
state.QP += 0.5*(x[i]*Nabla[i]+x[i]*f[i]);
print_state(state);
}
}
/* compute primal objective value */
state.QP = 0;
for(i = 0; i < n; i++)
state.QP += 0.5*(x[i]*Nabla[i]+x[i]*f[i]);
cleanup:
LIBQP_FREE(Nabla);
return(state);
}
我最喜歡的工具，這是[RCPP（http://www.rcpp.org/）。 –