Re: [ojAlgo-user] 答复: Report A Bug Of ojalgo finace library

[Anders P. <an...@op...>]

UNEXPLORED would mean the optimisation has not been executed (since you last modified the model). How did you get that?

/Anders




> On 9 May 2017, at 17:15, yifa wang <fag...@ho...> wrote:
> 
> hi,
> 
> I try to use ojalgo to do a portfolio optimization. The target is find an efficient frontier for 15 securities or more. I generate random returns for simulation. However, when I run the program, I get the state as "UNEXPLORED". Usually, we get OPTIMAL.  What does this mean? Is it possible to get the optimal weight for more than 50 securities?
> 
> Thanks. 
> Jerry 
> 
> 
> 
> 
> 发件人: lvz...@gt... <lvz...@gt...>
> 发送时间: 2017年5月8日 下午 8:20:21
> 收件人: oja...@li...
> 主题: [ojAlgo-user] Report A Bug Of ojalgo finace library
>  
> 
> Hi,
>       I have a problem when calculate best invest group by using markowitzModel in ojalgo with code below(for test);
> 
> 
> 
> 
>           for(int j = 0; j < 100; j ++)
>          { Builder<PrimitiveMatrix> tmpBuilder = PrimitiveMatrix.FACTORY.getBuilder(2, 2);
>     tmpBuilder.add(0, 0, 0.040000);
>     tmpBuilder.add(0, 1, 0.1000);
>     tmpBuilder.add(1, 0, 0.1000);
>     tmpBuilder.add(1, 1, 0.250000);
>         BasicMatrix covariances = tmpBuilder.build();
>         tmpBuilder = PrimitiveMatrix.FACTORY.getBuilder(2);
>         tmpBuilder.add(0, 0.20000);
>         tmpBuilder.add(1, 0.40000);
>         BasicMatrix expectedExcessReturns = tmpBuilder.build(); 
>         
>        MarketEquilibrium equilibrium = new MarketEquilibrium(covariances);
>         MarkowitzModel markowitzModel = new MarkowitzModel(equilibrium, expectedExcessReturns);
>         markowitzModel.setShortingAllowed(false);
>        // markowitzModel.optimiser().validate(true);
>         markowitzModel.setTargetReturn(new BigDecimal(0.2 + (0.002 * j)));
>         for (int i = 0; i < 2; i++) {
>             markowitzModel.setLowerLimit(i, new BigDecimal(0.00000));
>             markowitzModel.setUpperLimit(i, new BigDecimal(1.00000));
>         }
>         System.out.println(0.2 + (0.002 * j) + "," + markowitzModel.getReturnVariance() + " , "  +  markowitzModel.getMeanReturn()  + "," + markowitzModel.getWeights());
> 
> 
> }
> 
>      
>       when TargetReturn equal to 0.206,Variance equal to 0.04 , weights equal to [1.0, 0.0]
>       when TargetReturn  equal to  0.208, Variance equal to 0.25,weights equal to [0.0, 1.0]
>       Below  Relationship of TargetReturn And Target Variance（Y-Axis TargetReturn,X-AXis standard deviation） 
> 
> 
> 
> 吕子锋
> 
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