# Documentation

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## Example

Let us now turn our attention to an example of using Gurobi to solve a simple MIP model. Our example optimizes the following model:

 maximize x + y + 2 z subject to x + 2 y + 3 z 4 x + y 1 x, y, z binary
Note that this is the same model that was modeled and optimized in the C Interface section.

This is the complete source code for our example (also available in <installdir>/examples/R/mip.R)...

# Copyright 2020, Gurobi Optimization, LLC
#
# This example formulates and solves the following simple MIP model:
#  maximize
#        x +   y + 2 z
#  subject to
#        x + 2 y + 3 z <= 4
#        x +   y       >= 1
#        x, y, z binary

library(gurobi)

model <- list()

model$A <- matrix(c(1,2,3,1,1,0), nrow=2, ncol=3, byrow=T) model$obj        <- c(1,1,2)
model$modelsense <- 'max' model$rhs        <- c(4,1)
model$sense <- c('<', '>') model$vtype      <- 'B'

params <- list(OutputFlag=0)

result <- gurobi(model, params)

print('Solution:')
print(result$objval) print(result$x)

# Clear space
rm(model, result, params)