英文:
Optimization with multiple inequality constraints
问题
I am working on R, I need to set up a non linear optimization problem, my dataset has the following columns:
- Phs Code, that define univocally each pharmacy
- Score, that define the score associated to each pharmacy
- MKT, that define the market value generated by each pharmacy
The non-linear maximization problem should contain:
- decisional variable X(i) = the value to invest in pharmacy i
- Objective function:
MAX sum Y(i)
where ``Y(i) = (0.272*log( x(i) ) + score (i) ) sales in face of the investment X(i) - Constraints 1:
exp(0.0272*log( x(i) ) + score (i) ) < 0.8*MKT (i) - Constraints 2:
exp(0.0272*log( x(i) ) + score (i) ) - exp( 0.0272*log( x(i) + 1 ) + score (i) ) >= 1 - Constraints 3: sum of
X(i) < B
with B = budget - Constraints 4: X(i) > 0 for each i
#Objective functionobjfun <- function(x, score) 0.272 * log(x) + my_data$score# Define the constraintscon <- function(x, Score, MKT) {con1 <- exp(0.0272 * log(x) + Score) - 0.8 * MKTcon2 <- exp(0.0272 * log(x) + Score) - exp(0.0272 * log(x + 1) + Score) - 1con3 <- sum(x) - 18000000}# Set up the optimization problemx0 <- rep(1, nrow(my_data))lb <- rep(0, nrow(my_data))ub <- rep(Inf, nrow(my_data))problem <- list(objective = objfun,lb = lb,ub = ub,eval_g_ineq = con )result <- nloptr(x0 = x0,eval_f = problem$objective,lb = problem$lb,ub = problem$ub,eval_g_ineq = problem$eval_g_ineq,opts = list("algorithm" = "NLOPT_LN_COBYLA"))
Trying to change different things but keep bumping into errors. Not sure about where is the problem with my code. Really need some help
Thanks
英文:
I am working on R, I need to set up a non linear optimization problem, my dataset has the following columns:
- Phs Code, that define univocally each pharmacy
- Score, that define the score associated to each pharmacy
- MKT, that define the market value generated by each pharmacy
The non linear maximization problem should contain:
- decisional variable X(i) = the value to invest in pharmacy i
- Objective function:
MAX sum Y(i)
where ``Y(i) = (0.272*log( x(i) ) + score (i) ) sales in face of the investment X(i) - Constraints 1:
exp(0.0272*log( x(i) ) + score (i) ) < 0.8*MKT (i) - Constraints 2:
exp(0.0272*log( x(i) ) + score (i) ) - exp( 0.0272*log( x(i) + 1 ) + score (i) ) >= 1 - Constraints 3: sum of
X(i) < B
with B = budget - Constraints 4: X(i) > 0 for each i
--
#Objective functionobjfun <- function(x, score) 0.272 * log(x) + my_data$score# Define the constraintscon <- function(x, Score, MKT) {con1 <- exp(0.0272 * log(x) + Score) - 0.8 * MKTcon2 <- exp(0.0272 * log(x) + Score) - exp(0.0272 * log(x + 1) + Score) - 1con3 <- sum(x) - 18000000}# Set up the optimization problemx0 <- rep(1, nrow(my_data))lb <- rep(0, nrow(my_data))ub <- rep(Inf, nrow(my_data))problem <- list(objective = objfun,lb = lb,ub = ub,eval_g_ineq = con )result <- nloptr(x0 = x0,eval_f = problem$objective,lb = problem$lb,ub = problem$ub,eval_g_ineq = problem$eval_g_ineq,opts = list("algorithm" = "NLOPT_LN_COBYLA"))
Trying to change different things but keep bumping into errors. Not sure about where is the problem with my code. Really need some help
Thanks
答案1
得分: 1
假设您的数据有两个观察值，您应该这样做：```rscore <- c(12, 14) # 我没有您的数据MKT <- c(22, 26) # 我没有您的数据objfun <- function(x){ # 最大化问题，所以取负值-0.272 * sum(log(x) + score)}con <- function(x) {con1 <- exp(0.0272 * log(x) + score) - 0.8 * MKTcon2 <- exp(0.0272 * log(x) + score) - exp(0.0272 * log(x + 1) + score) - 1con3 <- sum(x) - 18000000c(con1, con2, con3)}x0 <- rep(1, 2)lb <- rep(0, 2)ub <- rep(Inf, 2)result <- nloptr(x0 = x0,eval_f = objfun,lb = lb,ub = ub,eval_g_ineq = con,opts = list("algorithm" = "NLOPT_LN_COBYLA"))
英文:
Assuming your data has two observations, you should do something like that:
score <- c(12, 14) # I don't have your dataMKT <- c(22, 26) # I don't have your dataobjfun <- function(x){ # minus for maximization-0.272 * sum(log(x) + score)}con <- function(x) {con1 <- exp(0.0272 * log(x) + score) - 0.8 * MKTcon2 <- exp(0.0272 * log(x) + score) - exp(0.0272 * log(x + 1) + score) - 1con3 <- sum(x) - 18000000c(con1, con2, con3)}x0 <- rep(1, 2)lb <- rep(0, 2)ub <- rep(Inf, 2)result <- nloptr(x0 = x0,eval_f = objfun,lb = lb,ub = ub,eval_g_ineq = con,opts = list("algorithm" = "NLOPT_LN_COBYLA"))
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