Optimization with multiple inequality constraints

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 function

    objfun <- function(x, score) 0.272 * log(x) + my_data$score


# Define the constraints

    con <- function(x, Score, MKT) {
      con1 <- exp(0.0272 * log(x) + Score) - 0.8 * MKT
      con2 <- exp(0.0272 * log(x) + Score) - exp(0.0272 * log(x + 1) + Score) - 1
      con3 <- sum(x) - 18000000
    }

# Set up the optimization problem

    x0 <- 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

Assuming your data has two observations, you should do something like that:

score <- c(12, 14) # I don't have your data
MKT   <- c(22, 26) # I don't have your data

objfun <- function(x){ # minus for maximization
  -0.272 * sum(log(x) + score)
}

con <- function(x) {
  con1 <- exp(0.0272 * log(x) + score) - 0.8 * MKT
  con2 <- exp(0.0272 * log(x) + score) - exp(0.0272 * log(x + 1) + score) - 1
  con3 <- sum(x) - 18000000
  c(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"))