[ojAlgo-user] Markowitz Model Problem - Optimization isssue

[Nilesh P. <nil...@gm...>]

Hi..this is slighly modifed version of the already posted issue on this
forum earlier.
I am using ojalgo 2.6 & also tried same code with new beta 2.7 version but
the issue still persists
Below code works fails if i set the expected return to
*markowitzModel.setTargetReturn(new
BigDecimal("0.01051787"));*
any value *between 0.01051787  and 0.003081388 (which is the lowest return
in the return matrix) gives same output.
*
Note: Values (expected rerurn) between *0.01051787  and 0.03360872 (max.
return as per return matrix) works perfectly fine.
*
Do revert in case of any further clarifications.
--------START CODE---------------
import java.math.BigDecimal;
import java.util.List;

import org.ojalgo.finance.portfolio.MarketEquilibrium;
import org.ojalgo.finance.portfolio.MarkowitzModel;
import org.ojalgo.matrix.*;
import org.ojalgo.matrix.store.BigDenseStore;
import org.ojalgo.matrix.store.MatrixStore;


public class TestMarkowitz {

public BasicMatrix getACovariances(double[][] returns){

int row = returns.length;
int col = returns[0].length;
MatrixStore<BigDecimal> a = BigDenseStore.FACTORY.makeZero(row,
col);
BasicMatrix covariances = new BigMatrix(a);

for(int i=1;i<=row;i++){
for(int j=i;j<=col;j++){
covariances = covariances.set(i-1, j-1,returns[i-1][j-1]);
covariances = covariances.set(j-1, i-1,returns[j-1][i-1]);
}

}
return covariances;
}


public static void main(String[] args)
{
int assetNum=16;


double[][] om={
{0.003330616,0.003524811,0.00386567,0.003656347,0.004494241,0.004623772,0.00458625,0.004365933,0,0,0,0,0,0,0,0},
{0.003524811,0.004274864,0.004372518,0.004135748,0.005144421,0.005292691,0.005249742,0.004997551,0,0,0,0,0,0,0,0},
{0.00386567,0.004372518,0.005114057,0.004535687,0.005641369,0.005803962,0.005756863,0.005480312,0,0,0,0,0,0,0,0},
{0.003656347,0.004135748,0.004535687,0.004728464,0.005511769,0.005670626,0.00562461,0.005354411,0,0,0,0,0,0,0,0},
{0.004494241,0.005144421,0.005641369,0.005511769,0.007284319,0.006518612,0.006359324,0.00635862,0,0,0,0,0,0,0,0},
{0.004623772,0.005292691,0.005803962,0.005670626,0.006518612,0.007542516,0.006562129,0.006561403,0,0,0,0,0,0,0,0},
{0.00458625,0.005249742,0.005756863,0.00562461,0.006359324,0.006562129,0.007513433,0.00640107,0,0,0,0,0,0,0,0},
{0.004365933,0.004997551,0.005480312,0.005354411,0.00635862,0.006561403,0.00640107,0.006805889,0,0,0,0,0,0,0,0},
{0,0,0,0,0,0,0,0,0.000152891,0.000120753,7.98783E-05,0.000116607,8.11225E-05,4.31112E-05,6.11874E-05,1.49376E-07},
{0,0,0,0,0,0,0,0,0.000120753,0.000215819,0.000108447,0.000158311,7.62318E-05,4.05121E-05,5.74986E-05,1.4037E-07},
{0,0,0,0,0,0,0,0,7.98783E-05,0.000108447,0.000251455,0.000104723,0.000105337,5.59796E-05,7.94514E-05,1.93963E-07},
{0,0,0,0,0,0,0,0,0.000116607,0.000158311,0.000104723,0.000285454,0.000109622,5.82568E-05,8.26835E-05,2.01854E-07},
{0,0,0,0,0,0,0,0,8.11225E-05,7.62318E-05,0.000105337,0.000109622,0.000110962,5.62808E-05,7.47961E-05,1.97502E-06},
{0,0,0,0,0,0,0,0,4.31112E-05,4.05121E-05,5.59796E-05,5.82568E-05,5.62808E-05,4.12145E-05,4.25652E-05,1.12395E-06},
{0,0,0,0,0,0,0,0,6.11874E-05,5.74986E-05,7.94514E-05,8.26835E-05,7.47961E-05,4.25652E-05,7.62351E-05,1.4937E-06},
{0,0,0,0,0,0,0,0,1.49376E-07,1.4037E-07,1.93963E-07,2.01854E-07,1.97502E-06,1.12395E-06,1.4937E-06,6.52443E-06}
};

TestMarkowitz tm = new TestMarkowitz();

BasicMatrix covariances = tm.getACovariances(om);

System.out.println(covariances);

BigDecimal riskAversion = new BigDecimal(1.0);

MarketEquilibrium marketEquilibrium = new
MarketEquilibrium(covariances,riskAversion);

MatrixStore<BigDecimal> ret_matrix = BigDenseStore.FACTORY.makeZero
(assetNum, 1);
BasicMatrix expectedExcessReturns1 = new BigMatrix(ret_matrix);

expectedExcessReturns1 = expectedExcessReturns1.set(0,0,0.03360872);
expectedExcessReturns1 = expectedExcessReturns1.set(1,0,0.027322319);
expectedExcessReturns1 = expectedExcessReturns1.set(2,0,0.027668137);
expectedExcessReturns1 = expectedExcessReturns1.set(3,0,0.03080239);
expectedExcessReturns1 = expectedExcessReturns1.set(4,0,0.025067124);
expectedExcessReturns1 = expectedExcessReturns1.set(5,0,0.016578507);
expectedExcessReturns1 = expectedExcessReturns1.set(6,0,0.022622714);
expectedExcessReturns1 = expectedExcessReturns1.set(7,0,0.028957183);
expectedExcessReturns1 = expectedExcessReturns1.set(8,0,0.009939058);
expectedExcessReturns1 = expectedExcessReturns1.set(9,0,0.010014445);
expectedExcessReturns1 = expectedExcessReturns1.set(10,0,0.011565874);
expectedExcessReturns1 = expectedExcessReturns1.set(11,0,0.011609169);
expectedExcessReturns1 = expectedExcessReturns1.set(12,0,0.006286505);
expectedExcessReturns1 = expectedExcessReturns1.set(13,0,0.004240681);
expectedExcessReturns1 = expectedExcessReturns1.set(14,0,0.006162067);
expectedExcessReturns1 = expectedExcessReturns1.set(15,0,0.003081388);

System.out.println("Return Matrix" + expectedExcessReturns1);

MarkowitzModel markowitzModel = new
MarkowitzModel(marketEquilibrium,expectedExcessReturns1);

*markowitzModel.setTargetReturn(new BigDecimal("0.01051787"));*

for(int i=0;i<assetNum;i++){
markowitzModel.setLowerLimit(i, new BigDecimal(0.0));
markowitzModel.setUpperLimit(i, new BigDecimal(1.0));
}

List<BigDecimal> re = markowitzModel.getWeights();


System.out.println("=======result====================");
for(int i=0;i<re.size();i++)
System.out.println(re.get(i));

System.out.println("=======result====================");
System.out.println(markowitzModel.getMeanReturn());
System.out.println(markowitzModel.getReturnVariance());
return;
}

}

--------END CODE---------------
-- 

Regards....Nilesh