# Copyright 2025, Gurobi Optimization, LLC## This example reads a MIP model from a file, adds artificial# variables to each constraint, and then minimizes the sum of the# artificial variables.  A solution with objective zero corresponds# to a feasible solution to the input model.# We can also use FeasRelax feature to do it. In this example, we# use minrelax=1, i.e. optimizing the returned model finds a solution# that minimizes the original objective, but only from among those# solutions that minimize the sum of the artificial variables.library(Matrix)library(gurobi)args<-commandArgs(trailingOnly=TRUE)if(length(args)<1){stop('Usage: Rscript feasopt.R filename\n')}# Set up parametersparams<-list()params$logfile<-'feasopt.log'# Read modelcat('Reading model',args[1],'...')model<-gurobi_read(args[1],params)cat('... done\n')# Create penaltiespenalties<-list()penalties$lb<-Infpenalties$ub<-Infpenalties$rhs<-rep(1,length(model$rhs))result<-gurobi_feasrelax(model,0,TRUE,penalties,params=params)# Display resultsif(result$feasobj>1e-6){cat('Model',args[1],'is infeasible within variable bounds\n')}else{cat('Model',args[1],'is feasible\n')}# Clear spacerm(params,model,penalties,result)