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Knapsack¶
Principles learned¶
- Create a generic model that uses data
- Read an instance from a file
- Write the solution in a file
Problem¶
The knapsack problem is defined as follows: given a set of items, each with a weight and a value, determine a subset of items in such a way that their total weight is less than a given bound and their total value is as large as possible. This problem is hard to solve in theory.
Download the exampleProgram¶
Note that the way to model is exactly the same than in integer programming: for each item, a 0-1 decision variable is defined which is equal to 1 if the item belongs to the knapsack and 0 otherwise.
Knapsack instances involving millions of objects can be tackled using LocalSolver.
- Execution:
- localsolver knapsack.lsp inFileName=instances/kp_100_1.in [lsTimeLimit=] [solFileName=]
/********** knapsack.lsp **********/
use io;
/* Reads instance data. */
function input() {
local usage = "Usage: localsolver knapsack.lsp "
+ "inFileName=inputFile [solFileName=outputFile] [lsTimeLimit=timeLimit]";
if (inFileName == nil) throw usage;
local inFile = io.openRead(inFileName);
nbItems = inFile.readInt();
weights[i in 0..nbItems-1] = inFile.readInt();
prices[i in 0..nbItems-1] = inFile.readInt();
knapsackBound = inFile.readInt();
}
/* Declares the optimization model. */
function model() {
// 0-1 decisions
x[i in 0..nbItems-1] <- bool();
// weight constraint
knapsackWeight <- sum[i in 0..nbItems-1](weights[i] * x[i]);
constraint knapsackWeight <= knapsackBound;
// maximize value
knapsackValue <- sum[i in 0..nbItems-1](prices[i] * x[i]);
maximize knapsackValue;
}
/* Parameterizes the solver. */
function param() {
if (lsTimeLimit == nil) lsTimeLimit = 20;
}
/* Writes the solution in a file */
function output() {
if (solFileName == nil) return;
local solFile = io.openWrite(solFileName);
solFile.println(knapsackValue.value);
for [i in 0..nbItems-1 : x[i].value == 1]
solFile.print(i + " ");
solFile.println();
}
- Execution (Windows)
- set PYTHONPATH=%LS_HOME%\bin\python knapsack.py instances\kp_100_1.in
- Execution (Linux)
- export PYTHONPATH=/opt/localsolver_9_5/bin/python knapsack.py instances/kp_100_1.in
########## knapsack.py ##########
import localsolver
import sys
if len(sys.argv) < 2:
print ("Usage: python knapsack.py inputFile [outputFile] [timeLimit]")
sys.exit(1)
def read_integers(filename):
with open(filename) as f:
return [int(elem) for elem in f.read().split()]
with localsolver.LocalSolver() as ls:
#
# Reads instance data
#
file_it = iter(read_integers(sys.argv[1]))
# Number of items
nb_items = next(file_it)
# Items properties
weights = [next(file_it) for i in range(nb_items)]
values = [next(file_it) for i in range(nb_items)]
# Knapsack bound
knapsack_bound = next(file_it)
#
# Declares the optimization model
#
model = ls.model
# Decision variables x[i]
x = [model.bool() for i in range(nb_items)]
# Weight constraint
knapsack_weight = model.sum(x[i]*weights[i] for i in range(nb_items))
model.constraint(knapsack_weight <= knapsack_bound)
# Maximize value
knapsack_value = model.sum(x[i]*values[i] for i in range(nb_items))
model.maximize(knapsack_value)
model.close()
#
# Parameterizes the solver
#
if len(sys.argv) >= 4: ls.param.time_limit = int(sys.argv[3])
else: ls.param.time_limit = 20
ls.solve()
#
# Writes the solution in a file
#
if len(sys.argv) >= 3:
with open(sys.argv[2], 'w') as f:
f.write("%d\n" % knapsack_value.value)
for i in range(nb_items):
if x[i].value != 1: continue
f.write("%d " % i)
f.write("\n")
- Compilation / Execution (Windows)
- cl /EHsc knapsack.cpp -I%LS_HOME%\include /link %LS_HOME%\bin\localsolver95.libknapsack instances\kp_100_1.in
- Compilation / Execution (Linux)
- g++ knapsack.cpp -I/opt/localsolver_9_5/include -llocalsolver95 -lpthread -o knapsack./knapsack instances/kp_100_1.in
//********* knapsack.cpp *********
#include <iostream>
#include <fstream>
#include <vector>
#include "localsolver.h"
using namespace localsolver;
using namespace std;
class Knapsack {
public:
// Number of items.
int nbItems;
// Items properties.
vector<lsint> weights;
vector<lsint> values;
// Knapsack bound
lsint knapsackBound;
// LocalSolver.
LocalSolver localsolver;
// Decision variables.
vector<LSExpression> x;
// Objective.
LSExpression knapsackValue;
// Solution (items in the knapsack).
vector<int> solution;
// Reads instance data.
void readInstance(const string& fileName) {
ifstream infile;
infile.exceptions(ifstream::failbit | ifstream::badbit);
infile.open(fileName.c_str());
infile >> nbItems;
weights.resize(nbItems);
for (int i = 0; i < nbItems; i++)
infile >> weights[i];
values.resize(nbItems);
for (int i = 0; i < nbItems; i++)
infile >> values[i];
infile >> knapsackBound;
}
void solve(int limit) {
// Declares the optimization model.
LSModel model = localsolver.getModel();
// Decision variables x[i]
x.resize(nbItems);
for (int i = 0; i < nbItems; i++) {
x[i] = model.boolVar();
}
// Weight constraint
LSExpression knapsackWeight = model.sum();
for (int i = 0; i < nbItems; i++) {
LSExpression itemWeight = x[i]*weights[i];
knapsackWeight += itemWeight;
}
model.constraint(knapsackWeight <= knapsackBound);
// Maximize value
knapsackValue = model.sum();
for (int i = 0; i < nbItems; i++) {
LSExpression itemValue = x[i]*values[i];
knapsackValue += itemValue;
}
model.maximize(knapsackValue);
model.close();
// Parameterizes the solver.
localsolver.getParam().setTimeLimit(limit);
localsolver.solve();
solution.clear();
for (int i = 0; i < nbItems; ++i)
if (x[i].getValue() == 1)
solution.push_back(i);
}
// Writes the solution in a file
void writeSolution(const string& fileName) {
ofstream outfile;
outfile.exceptions(ofstream::failbit | ofstream::badbit);
outfile.open(fileName.c_str());
outfile << knapsackValue.getValue() << endl;
for (unsigned int i = 0; i < solution.size(); ++i)
outfile << solution[i] << " ";
outfile << endl;
}
};
int main(int argc, char** argv) {
if (argc < 2) {
cerr << "Usage: knapsack inputFile [outputFile] [timeLimit]" << endl;
return 1;
}
const char* instanceFile = argv[1];
const char* solFile = argc > 2 ? argv[2] : NULL;
const char* strTimeLimit = argc > 3 ? argv[3] : "20";
try {
Knapsack model;
model.readInstance(instanceFile);
model.solve(atoi(strTimeLimit));
if (solFile != NULL) model.writeSolution(solFile);
return 0;
} catch (const exception& e) {
cerr << "An error occurred: " << e.what() << endl;
return 1;
}
}
- Compilation / Execution (Windows)
- copy %LS_HOME%\bin\localsolvernet.dll .csc Knapsack.cs /reference:localsolvernet.dllKnapsack instances\kp_100_1.in
/********** Knapsack.cs **********/
using System;
using System.IO;
using System.Collections.Generic;
using localsolver;
public class Knapsack : IDisposable
{
// Number of items
int nbItems;
// Items properties.
int[] weights;
int[] values;
// Knapsack bound.
int knapsackBound;
// Solver.
LocalSolver localsolver;
// LS Program variables.
LSExpression[] x;
// Objective.
LSExpression knapsackValue;
// Solutions (items in the knapsack).
List<int> solutions;
public Knapsack()
{
localsolver = new LocalSolver();
}
// Reads instance data.
void ReadInstance(string fileName)
{
using (StreamReader input = new StreamReader(fileName))
{
nbItems = int.Parse(input.ReadLine());
weights = new int[nbItems];
values = new int[nbItems];
string[] splittedWeights = input.ReadLine().Split(' ');
if (splittedWeights.Length < nbItems)
throw new Exception("Wrong number of item weights");
for (int i = 0; i < nbItems; i++)
weights[i] = int.Parse(splittedWeights[i]);
string[] splittedValues = input.ReadLine().Split(' ');
if (splittedValues.Length < nbItems)
throw new Exception("Wrong number of item values");
for (int i = 0; i < nbItems; i++)
values[i] = int.Parse(splittedValues[i]);
knapsackBound = int.Parse(input.ReadLine());
}
}
public void Dispose()
{
if (localsolver != null)
localsolver.Dispose();
}
void Solve(int limit)
{
// Declares the optimization model.
LSModel model = localsolver.GetModel();
// Decision variables x[i]
x = new LSExpression[nbItems];
for (int i = 0; i < nbItems; i++)
{
x[i] = model.Bool();
}
// weight constraint
LSExpression knapsackWeight = model.Sum();
for (int i = 0; i < nbItems; i++)
{
knapsackWeight.AddOperand(x[i] * weights[i]);
}
model.Constraint(knapsackWeight <= knapsackBound);
// maximize value
knapsackValue = model.Sum();
for (int i = 0; i < nbItems; i++)
{
knapsackValue.AddOperand(x[i] * values[i]);
}
model.Maximize(knapsackValue);
model.Close();
// Parameterizes the solver.
localsolver.GetParam().SetTimeLimit(limit);
localsolver.Solve();
solutions = new List<int>();
for (int i = 0; i < nbItems; ++i)
{
if (x[i].GetValue() == 1) solutions.Add(i);
}
}
// Writes the solution in a file
void WriteSolution(string fileName)
{
using (StreamWriter output = new StreamWriter(fileName))
{
output.WriteLine(knapsackValue.GetValue());
for (int i = 0; i < solutions.Count; ++i)
output.Write(solutions[i] + " ");
output.WriteLine();
}
}
public static void Main(string[] args)
{
if (args.Length < 1)
{
Console.WriteLine("Usage: Knapsack inputFile [solFile] [timeLimit]");
Environment.Exit(1);
}
string instanceFile = args[0];
string outputFile = args.Length > 1 ? args[1] : null;
string strTimeLimit = args.Length > 2 ? args[2] : "20";
using (Knapsack model = new Knapsack())
{
model.ReadInstance(instanceFile);
model.Solve(int.Parse(strTimeLimit));
if (outputFile != null)
model.WriteSolution(outputFile);
}
}
}
- Compilation / Execution (Windows)
- javac Knapsack.java -cp %LS_HOME%\bin\localsolver.jarjava -cp %LS_HOME%\bin\localsolver.jar;. Knapsack instances\kp_100_1.in
- Compilation / Execution (Linux)
- javac Knapsack.java -cp /opt/localsolver_9_5/bin/localsolver.jarjava -cp /opt/localsolver_9_5/bin/localsolver.jar:. Knapsack instances/kp_100_1.in
/********** Knapsack.java **********/
import java.util.*;
import java.io.*;
import localsolver.*;
public class Knapsack {
// Number of items.
private int nbItems;
// Items properties.
private int[] weights;
private int[] values;
// Knapsack bound
private int knapsackBound;
// Solver.
private final LocalSolver localsolver;
// LS Program variables.
private LSExpression[] x;
// Objective.
private LSExpression knapsackValue;
// Solutions (classes at each position).
private List<Integer> solutions;
private Knapsack(LocalSolver localsolver) {
this.localsolver = localsolver;
}
// Reads instance data.
private void readInstance(String fileName) throws IOException {
try (Scanner input = new Scanner(new File(fileName))) {
nbItems = input.nextInt();
weights = new int[nbItems];
for (int i = 0; i < nbItems; i++) {
weights[i] = input.nextInt();
}
values = new int[nbItems];
for (int i = 0; i < nbItems; i++) {
values[i] = input.nextInt();
}
knapsackBound = input.nextInt();
}
}
private void solve(int limit) {
// Declares the optimization model.
LSModel model = localsolver.getModel();
// boolean variables x[i]
x = new LSExpression[nbItems];
for (int i = 0; i < nbItems; i++) {
x[i] = model.boolVar();
}
// weight constraint
LSExpression knapsackWeight = model.sum();
for (int i = 0; i < nbItems; i++) {
LSExpression itemWeight = model.prod(x[i], weights[i]);
knapsackWeight.addOperand(itemWeight);
}
model.constraint(model.leq(knapsackWeight, knapsackBound));
// maximize value
knapsackValue = model.sum();
for (int i = 0; i < nbItems; i++) {
LSExpression itemValue = model.prod(x[i], values[i]);
knapsackValue.addOperand(itemValue);
}
model.maximize(knapsackValue);
model.close();
// Parameterizes the solver.
localsolver.getParam().setTimeLimit(limit);
localsolver.solve();
solutions = new ArrayList<Integer>();
for (int i = 0; i < nbItems; i++)
if (x[i].getValue() == 1)
solutions.add(i);
}
// Writes the solution in a file
private void writeSolution(String fileName) throws IOException {
try (PrintWriter output = new PrintWriter(fileName)) {
output.println(knapsackValue.getValue());
for (int i = 0; i < solutions.size(); ++i)
output.print(solutions.get(i) + " ");
output.println();
}
}
public static void main(String[] args) {
if (args.length < 1) {
System.err.println("Usage: java Knapsack inputFile [outputFile] [timeLimit]");
System.exit(1);
}
String instanceFile = args[0];
String outputFile = args.length > 1 ? args[1] : null;
String strTimeLimit = args.length > 2 ? args[2] : "20";
try (LocalSolver localsolver = new LocalSolver()) {
Knapsack model = new Knapsack(localsolver);
model.readInstance(instanceFile);
model.solve(Integer.parseInt(strTimeLimit));
if (outputFile != null) {
model.writeSolution(outputFile);
}
} catch(Exception ex) {
System.err.println(ex);
ex.printStackTrace();
System.exit(1);
}
}
}