CODEWITHSUNDEEP
rmathematical-optimization
wcm
wcm

Reputation: 9281

Optimization packages for R

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

Upvotes: 14

Views: 25731

Answers (9)

jean valayer
jean valayer

Reputation: 1

Ideally inject a dynamic momentum in nlopt or any optimizer, involving inertia and viscosity. Does not seem available in .R

Upvotes: 0

Shibaprasad
Shibaprasad

Reputation: 1332

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))

Upvotes: 4

nba2020
nba2020

Reputation: 688

Have a look on the NLoptr package. It has quite an extensive documentation with examples and lots of algorithms to choose from, depending what problem you are trying to resolve (etc. linear, non-linear, constraint)

Upvotes: 3

beddotcom
beddotcom

Reputation: 507

I like Gurobi. It's very expensive for a license, but it can be obtained through many universities. See here http://www.gurobi.com/products/modeling-languages/r

Upvotes: 3

gappy
gappy

Reputation: 10243

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.

Upvotes: 21

dwstu
dwstu

Reputation: 869

you should also try the Rglpk package solve LP problems with GLPK (GNU Linear Programming Kit).

An example:

## Simple linear program.
## maximize:   2 x_1 + 4 x_2 + 3 x_3
## subject to: 3 x_1 + 4 x_2 + 2 x_3 <= 60
##             2 x_1 +   x_2 +   x_3 <= 40
##               x_1 + 3 x_2 + 2 x_3 <= 80
##               x_1, x_2, x_3 are non-negative real numbers

obj <- c(2, 4, 3)
mat <- matrix(c(3, 2, 1, 4, 1, 3, 2, 2, 2), nrow = 3)
dir <- c("<=", "<=", "<=")
rhs <- c(60, 40, 80)
max <- TRUE

Rglpk_solve_LP(obj, mat, dir, rhs, max = max)

R output:
(Note that $status an integer with status information about the solution returned. If the control parameter canonicalize_status is set (the default) then it will return 0 for the optimal solution being found, and non-zero otherwise. If the control parameter is set to FALSE it will return the GLPK status codes). 

$optimum
[1] 76.66667

$solution
[1]  0.000000  6.666667 16.666667

$status
[1] 0

Upvotes: 8

dwstu
dwstu

Reputation: 869

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

Upvotes: 8

JD Long
JD Long

Reputation: 60746

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.

Upvotes: 7

Galwegian
Galwegian

Reputation: 42227

I have used linprog for linear problems in the past.

Upvotes: 4

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