[ojAlgo-user] Knapsack problem with binary variables

[Luke L. <luk...@gm...>]

I am trying to solve a simple knapsack problem (maximise value given
weight limit) with ojalgo.  See code below.  The code works as I would
expected when var[i] are not set to integers.  I.e. the result is
"OPTIMAL (0) { 1.0, 0.25, 3.0, 0.0, 0.0, 0.75 }" which I interpret as
meaning "take one unit of item 0 and 0.25 of item 1".

I want to restrict var[i] to being 0 or 1.  But when I uncomment the
line "vars[i].setInteger(true);", I get "OPTIMAL (1) { 0.0, 0.0, 0.0,
3.0, 0.0, 1.0, 1.0 }".  I was expecting "OPTIMAL (0) { 1.0, 0.0, 0..."
 i.e. "take one unit of item 0 and none of item 1".

Am I doing something wrong here?


.........................................................................................................................................
package knapsack;


import static org.ojalgo.constant.BigMath.*;

import java.math.BigDecimal;
import org.ojalgo.optimisation.*;
import org.ojalgo.optimisation.OptimisationSolver.Result;
import org.ojalgo.optimisation.linear.LinearExpressionsModel;

public class Knapsack {
	
	static class Item{
		final BigDecimal value;
		final BigDecimal weight;
		public Item(int value, int weight) {			
			this.value = new BigDecimal(value);
			this.weight = new BigDecimal(weight);
		}		
	}

	
	public static void main(String[] args) {
		
		BigDecimal maxWeight = new  BigDecimal(3);
		Item[] items = {new Item(20, 2), new Item(30, 4)};
				
		Variable[] vars = new Variable[items.length];
		for(int i =0; i < vars.length; i++){			
			vars[i] = new Variable(String.valueOf(i));
			//vars[i].setInteger(true);
			
			vars[i].upper(ONE).lower(ZERO);
			vars[i].setContributionWeight(items[i].value);			
		}
		
						
		LinearExpressionsModel model = new LinearExpressionsModel(vars);		
		Expression totalWeight = model.addEmptyLinearExpression("total weight");
		
		for(int i =0; i < items.length; i++){			
			totalWeight.setLinearFactor(i, items[i].weight);
		}		
		totalWeight.setUpperLimit(maxWeight);
		totalWeight.setLowerLimit(ZERO);
		
		
		System.out.println(totalWeight);
		
		model.maximise();
		OptimisationSolver solver  = model.getDefaultSolver();		

		Result result = solver.solve();
		System.out.println(result);
		System.out.println(model.maximise());
		System.out.println(model.minimise());
	}
}