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mip2.R
# Copyright 2023, Gurobi Optimization, LLC # # This example reads a MIP model from a file, solves it and # prints the objective values from all feasible solutions # generated while solving the MIP. Then it creates the fixed # model and solves that model. library(Matrix) library(gurobi) args <- commandArgs(trailingOnly = TRUE) if (length(args) < 1) { stop('Usage: Rscript mip2.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 <- dim(model$A)[[2]] intvars <- which(model$vtype != 'C') numintvars <- length(intvars) if (numintvars < 1) { stop('All model\'s variables are continuous, nothing to do\n') } # Optimize params <- list() params$poolsolutions <- 20 result <- gurobi(model, params) # Capture solution information if (result$status != 'OPTIMAL') { cat('Optimization finished with status', result$status, '\n') stop('Stop now\n') } # Iterate over the solutions if ('pool' %in% names(result)) { solcount <- length(result$pool) for (k in 1:solcount) { cat('Solution', k, 'has objective:', result$pool[[k]]$objval, '\n') } } else { solcount <- 1 cat('Solution 1 has objective:', result$objval, '\n') } # Convert to fixed model for (j in 1:numvars) { if (model$vtype[j] != 'C') { t <- floor(result$x[j]+0.5) model$lb[j] <- t model$ub[j] <- t } } # Solve the fixed model result2 <- gurobi(model, params) if (result2$status != 'OPTIMAL') { stop('Error: fixed model isn\'t optimal\n') } if (abs(result$objval - result2$objval) > 1e-6 * (1 + abs(result$objval))) { stop('Error: Objective values differ\n') } # Print values of non-zero variables for (j in 1:numvars) { if (abs(result2$x[j]) < 1e-6) next varnames <- '' if ('varnames' %in% names(model)) { varnames <- model$varnames[j] } else { varnames <- sprintf('X%d', j) } cat(format(varnames, justify='left', width=10),':', format(result2$x[j], justify='right', digits=2, width=10), '\n') } # Clear space rm(model, params, result, result2)