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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# R Interface for Interior Point OPTimizer (IPOPT)
[](https://travis-ci.org/Non-Contradiction/ipoptjlr)
[](https://ci.appveyor.com/project/Non-Contradiction/ipoptjlr)
Package `ipoptjlr` is an R interface to the Ipopt nonlinear solver.
It provides a simple high-level wrapper for
'Julia' package 'Ipopt.jl' (see <https://github.com/JuliaOpt/Ipopt.jl>
for more information).
## Installation
`ipoptjlr` can be installed from `Github` by using `devtools`:
```{r, eval=FALSE}
devtools::install_github("Non-Contradiction/ipoptjlr")
```
## Usage
Here is one small example in using `ipoptjlr`:
```{r}
x <- c(1.0, 5.0, 5.0, 1.0)
x_L <- c(1.0, 1.0, 1.0, 1.0)
x_U = c(5.0, 5.0, 5.0, 5.0)
g_L <- c(25.0, 40.0)
g_U <- c(2.0e19, 40.0)
eval_f <- function(x){
x[1] * x[4] * (x[1] + x[2] + x[3]) + x[3]
}
eval_g <- function(x){
g = rep(0, 2)
g[1] = x[1] * x[2] * x[3] * x[4]
g[2] = x[1]^2 + x[2]^2 + x[3]^2 + x[4]^2
g
}
eval_grad_f <- function(x){
grad_f = rep(0, 4)
grad_f[1] = x[1] * x[4] + x[4] * (x[1] + x[2] + x[3])
grad_f[2] = x[1] * x[4]
grad_f[3] = x[1] * x[4] + 1
grad_f[4] = x[1] * (x[1] + x[2] + x[3])
grad_f
}
jac_g1 <- function(x){
rows = rep(0, 8)
cols = rep(0, 8)
rows[1] = 1; cols[1] = 1
rows[2] = 1; cols[2] = 2
rows[3] = 1; cols[3] = 3
rows[4] = 1; cols[4] = 4
# Constraint (row) 2
rows[5] = 2; cols[5] = 1
rows[6] = 2; cols[6] = 2
rows[7] = 2; cols[7] = 3
rows[8] = 2; cols[8] = 4
list(rows, cols)
}
jac_g2 <- function(x){
values = rep(0, 8)
# Constraint (row) 1
values[1] = x[2]*x[3]*x[4] # 1,1
values[2] = x[1]*x[3]*x[4] # 1,2
values[3] = x[1]*x[2]*x[4] # 1,3
values[4] = x[1]*x[2]*x[3] # 1,4
# Constraint (row) 2
values[5] = 2*x[1] # 2,1
values[6] = 2*x[2] # 2,2
values[7] = 2*x[3] # 2,3
values[8] = 2*x[4] # 2,4
values
}
h1 <- function(x){
# Symmetric matrix, fill the lower left triangle only
rows = rep(0, 10)
cols = rep(0, 10)
idx = 1
for (row in 1:4) {
for (col in 1:row) {
rows[idx] = row
cols[idx] = col
idx = idx + 1
}
}
list(rows, cols)
}
h2 <- function(x, obj_factor, lambda){
values = rep(0, 10)
# Again, only lower left triangle
# Objective
values[1] = obj_factor * (2*x[4]) # 1,1
values[2] = obj_factor * ( x[4]) # 2,1
values[3] = 0 # 2,2
values[4] = obj_factor * ( x[4]) # 3,1
values[5] = 0 # 3,2
values[6] = 0 # 3,3
values[7] = obj_factor * (2*x[1] + x[2] + x[3]) # 4,1
values[8] = obj_factor * ( x[1]) # 4,2
values[9] = obj_factor * ( x[1]) # 4,3
values[10] = 0 # 4,4
# First constraint
values[2] = values[2] + lambda[1] * (x[3] * x[4]) # 2,1
values[4] = values[4] + lambda[1] * (x[2] * x[4]) # 3,1
values[5] = values[5] + lambda[1] * (x[1] * x[4]) # 3,2
values[7] = values[7] + lambda[1] * (x[2] * x[3]) # 4,1
values[8] = values[8] + lambda[1] * (x[1] * x[3]) # 4,2
values[9] = values[9] + lambda[1] * (x[1] * x[2]) # 4,3
# Second constraint
values[1] = values[1] + lambda[2] * 2 # 1,1
values[3] = values[3] + lambda[2] * 2 # 2,2
values[6] = values[6] + lambda[2] * 2 # 3,3
values[10] = values[10] + lambda[2] * 2 # 4,4
values
}
library(ipoptjlr)
ipopt_setup()
IPOPT(x, x_L, x_U, g_L, g_U, eval_f, eval_g, eval_grad_f, jac_g1, jac_g2, h1, h2)
```