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