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poolsearch.R
# Copyright 2018, Gurobi Optimization, LLC # # We find alternative epsilon-optimal solutions to a given knapsack # problem by using PoolSearchMode library(Matrix) library(gurobi) # define primitive data groundSetSize <- 10 objCoef <- c(32, 32, 15, 15, 6, 6, 1, 1, 1, 1) knapsackCoef <- c(16, 16, 8, 8, 4, 4, 2, 2, 1, 1) Budget <- 33 # Initialize model model <- list() model$modelsense <- 'max' model$modelname <- 'poolsearch' # Set variables model$obj <- objCoef model$vtype <- 'B' model$lb <- 0 model$ub <- 1 model$varnames <- sprintf('El%d', seq(1,groundSetSize)) # Build constraint matrix model$A <- spMatrix(1, groundSetSize, i = rep(1,groundSetSize), j = 1:groundSetSize, x = knapsackCoef) model$rhs <- c(Budget) model$sense <- c('<') model$constrnames <- c('Budget') # Set poolsearch parameters params <- list() params$PoolSolutions <- 1024 params$PoolGap <- 0.10 params$PoolSearchMode <- 2 # Save problem gurobi_write(model, 'poolsearch_R.lp') # Optimize result <- gurobi(model, params) # Capture solution information if (result$status != 'OPTIMAL') { cat('Optimization finished with status', result$status, '\n') stop('Stop now\n') } # Print best solution cat('Selected elements in best solution:\n') cat(model$varnames[which(result$x>=0.9)],'\n') # Print all solution objectives and best furth solution if ('pool' %in% names(result)) { solcount <- length(result$pool) cat('Number of solutions found:', solcount, '\n') cat('Objective values for first', solcount, 'solutions:\n') for (k in 1:solcount) { cat(result$pool[[k]]$objval,' ',sep='') } cat('\n') if (solcount >= 4) { cat('Selected elements in fourth best solution:\n') cat(model$varnames[which(result$pool[[4]]$xn >= 0.9)], '\n') } } else { solcount <- 1 cat('Number of solutions found:', solcount, '\n') cat('Solution 1 has objective:', result$objval, '\n') } # Clean up rm(model, params, result)