我使用Rglpk来解决线性规划问题,但其结果似乎很奇怪。我将其更改为lpSolve,两个结果不同。
请评论Rglpk并取消注释lpSolve语句,将解算器更改为lpSolve。
# Lo, S.-F., & Lu, W.-M. (2009). An integrated performance evaluation of financial holding companies in Taiwan.
# European Journal of Operational Research, 198(1), 341–350. doi:10.1016/j.ejor.2008.09.006
sbm = function(X,Y)
{
# Here X is N * m matrix, Y is N*s matrix.
library(Rglpk)
# require(lpSolve)
N = nrow(X)
m = ncol(X)
s = ncol(Y)
# variables are
# t
# gamma_j,j=1..N
# s_i^(-),i=1..m
# s_r^(+),r=1..s
efficiency = numeric(N)
max_positive_y = apply(Y[,1:s], MARGIN = 2, function(x) max(x[x>0]))
min_positive_y = apply(Y[,1:s], MARGIN = 2, function(x) min(x[x>0]))
dir = rep("==",1+m+s+1)
rhs = c(1,rep(0,m),rep(0,s),0)
for(i in 1:N)
{
x = X[i,]
y = Y[i,]
#variables
coef_t = 1
coef_gamma = rep(0,N)
coef_s_i = -1/(m * x)
coef_s_r = rep(0,s)
obj = c(coef_t,coef_gamma,coef_s_i,coef_s_r)
coef_constraint1_s=y
for(r in 1:s)
{
if(y[r]<0){
coef_constraint1_s[r] =
min_positive_y[r] * (max_positive_y[r] - min_positive_y[r])/
(max_positive_y[r] - y[r])
}
}
constraint1 = c(1, rep(0,N), rep(0,m) , 1/(s*coef_constraint1_s))
constraint2 = cbind(-x, t(X), diag(m), matrix(0,m,s))
constraint3 = cbind(-y, t(Y), matrix(0,s,m), -diag(s))
constraint4 = c(-1, rep(1,N), rep(0,m), rep(0,s))
mat = rbind(constraint1,constraint2,constraint3,constraint4)
results = Rglpk_solve_LP(obj = obj,mat = mat,dir = dir,rhs = rhs,max = FALSE)
efficiency[i] = results$optimum
# results <- lp("min", obj, mat, dir, rhs)
# efficiency[i] = results$objval
}
efficiency
}