Optimization packages for R

Question

Does anyone know of any optimization packages out there for R (similar to NUOPT for S+)?

Answer 1

Another package is ompr. An advantage of this package is there are many solvers that can be used and binary, continuous, integer all variables can easily be added. A simple example:

library(tidyverse)
library(ompr)
library(ompr.roi)


model <-  MIPModel() %>%
  add_variable(x1, type = "integer") %>%
  add_variable(x2, type = "integer") %>%
  set_bounds(x1, lb = 0) %>%
  set_bounds(x2, lb = 0) %>%
  set_objective(x1 - x2, "max") %>%
  add_constraint(x1 + 2*x2 <= 150) %>%
  add_constraint(x1 >= 30) %>%
  add_constraint(x2 >= 40)

Solving with glpk:

library(ROI.plugin.glpk)
result <- solve_model(model, with_ROI(solver = "glpk", verbose = TRUE))

get_solution(result, x1)
get_solution(result, x2)

It can also be solved with other solvers like symphony where the gap_limit can be set in case the problem is complex and will take many iterations to converge:

library(ROI.plugin.symphony)
result <- solve_model(model, with_ROI(solver = "symphony",
                                      verbosity=-1, gap_limit=1.5))

Answer 2

R has many, many packages for optimization; check the CRAN Task view on Optimization: http://cran.r-project.org/web/views/Optimization.html. Of course, for nonlinear programs, there is optim(), which is standard and includes Broyden-Fletcher-Goldfarb-Shanno's algorithm, and Nelder-Mead. It's a good first start.

Answer 3

Try lpSolve with R.

A simple example:

# Maximize 
#   x1 + 9 x2 +   x3 
# Subject to: 
#   x1 + 2 x2 + 3 x3 <= 9
# 3 x1 + 2 x2 + 2 x3 <= 15
f.obj <- c(1, 9, 3)
f.con <- matrix(c(1, 2, 3, 3, 2, 2), nrow = 2, byrow = TRUE)
f.dir <- c("<=", "<=")
f.rhs <- c(9, 15)

lp("max", f.obj, f.con, f.dir, f.rhs)
lp("max", f.obj, f.con, f.dir, f.rhs)$solution

Answer 4

Linprog, mentioned by Galwegian, focuses on linear programming via the simplex algorithm. In addition you may be interested in fPortfolio if you are doing portfolio optimization.