Try our new documentation site.


lpmod.R


# Copyright 2023, Gurobi Optimization, LLC
#
# This example reads an LP model from a file and solves it.
# If the model can be solved, then it finds the smallest positive variable,
# sets its upper bound to zero, and resultolves the model two ways:
# first with an advanced start, then without an advanced start
# (i.e. 'from scratch').

library(Matrix)
library(gurobi)

args <- commandArgs(trailingOnly = TRUE)
if (length(args) < 1) {
  stop('Usage: Rscript lpmod.R filename\n')
}

# Read model
cat('Reading model',args[1],'...')
model <- gurobi_read(args[1])
cat('... done\n')

# Determine whether it is an LP
if ('multiobj'  %in% names(model) ||
    'sos'       %in% names(model) ||
    'pwlobj'    %in% names(model) ||
    'cones'     %in% names(model) ||
    'quadcon'   %in% names(model) ||
    'genconstr' %in% names(model)   ) {
  stop('The model is not a linear program\n')
}

# Detect set of non-continuous variables
intvars    <- which(model$vtype != 'C')
numintvars <- length(intvars)
if (numintvars > 0) {
  stop('problem is a MIP, nothing to do\n')
}

# Optimize
result <- gurobi(model)
if (result$status != 'OPTIMAL') {
  cat('This model cannot be solved because its optimization status is',
      result$status, '\n')
  stop('Stop now\n')
}

# Recover number of variables in model
numvars   <- ncol(model$A)

# Ensure bounds array is initialized
if (is.null(model$lb)) {
  model$lb <- rep(0, numvars)
}
if (is.null(model$ub)) {
  model$ub <- rep(Inf, numvars)
}

# Find smallest (non-zero) variable value with zero lower bound
x      <- replace(result$x, result$x < 1e-4, Inf)
x      <- replace(x, model$lb > 1e-6, Inf)
minVar <- which.min(x)
minVal <- x[minVar]

# Get variable name
varname <- ''
if (is.null(model$varnames)) {
  varname <- sprintf('C%d',minVar)
} else {
  varname <- model$varnames[minVar]
}

cat('\n*** Setting', varname, 'from', minVal, 'to zero ***\n\n')
model$ub[minVar] <- 0

# Set advance start basis information
model$vbasis <- result$vbasis
model$cbasis <- result$cbasis

result2   <- gurobi(model)
warmCount <- result2$itercount
warmTime  <- result2$runtime

# Reset-advance start information
model$vbasis <- NULL
model$cbasis <- NULL

result2   <- gurobi(model)
coldCount <- result2$itercount
coldTime  <- result2$runtime

cat('\n*** Warm start:', warmCount, 'iterations,', warmTime, 'seconds\n')
cat('\n*** Cold start:', coldCount, 'iterations,', coldTime, 'seconds\n')

# Clear space
rm(model, result, result2)

Try Gurobi for Free

Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.

Evaluation License
Get a free, full-featured license of the Gurobi Optimizer to experience the performance, support, benchmarking and tuning services we provide as part of our product offering.
Academic License
Gurobi supports the teaching and use of optimization within academic institutions. We offer free, full-featured copies of Gurobi for use in class, and for research.
Cloud Trial

Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.

Search