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### sensitivity.R

# Copyright 2018, Gurobi Optimization, LLC
#
# A simple sensitivity analysis example which reads a MIP model
# from a file and solves it. Then each binary variable is set
# to 1-X, where X is its value in the optimal solution, and
# the impact on the objective function value is reported.

library(Matrix)
library(gurobi)

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

cat('... done\n')

# Detect set of non-continous variables
numvars    <- ncol(model$A) intvars <- which(model$vtype != 'C')
numintvars <- length(intvars)
if (numintvars < 1) {
stop('All model\'s variables are continuous, nothing to do\n')
}

# Optimize
result <- gurobi(model)

# Capture solution information
if (result$status != 'OPTIMAL') { cat('Optimization finished with status', result$status, '\n')
stop('Stop now\n')
}
origx       <- result$x origobjval <- result$objval

# create lb and ub if they do not exists, and set them to default values
if (!('lb' %in% names(model))) {
model$lb <- numeric(numvars) } if (!('ub' %in% names(model))) { # This line is not needed, as we must have ub defined model$ub <- Inf + numeric(numvars)
}

# Disable output for subsequent solves
params            <- list()
params$OutputFlag <- 0 # Iterate through unfixed binary variables in the model for (j in 1:numvars) { if (model$vtype[j] != 'B' &&
model$vtype[j] != 'I' ) next if (model$vtype[j] == 'I') {
if (model$lb[j] != 0.0) next if (model$ub[j] != 1.0)     next
} else {
if (model$lb[j] > 0.0) next if (model$ub[j] < 1.0)      next
}

# Update MIP start for all variables
model$start <- origx # Set variable to 1-X, where X is its value in optimal solution if (origx[j] < 0.5) { model$start[j] <- 1
model$lb[j] <- 1 } else { model$start[j] <- 0
model$ub[j] <- 0 } # Optimize result <- gurobi(model, params) # Display result varnames <- '' if ('varnames' %in% names(model)) { varnames <- model$varnames[j]
} else {
varnames <- sprintf('%s%d', model$vtype[j], j) } gap <- 0 if (result$status != 'OPTIMAL') {
gap <- Inf
} else {
gap <- result$objval - origobjval } cat('Objective sensitivity for variable', varnames, 'is', gap, '\n') # Restore original bounds model$lb[j] <- 0
model\$ub[j] <- 1
}

# Clear space
rm(model, params, result, origx)


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