请将以下代码重构为一个段落，并保持原有的格式：
```matlab
p = Portfolio(‘AssetMean’，[0.3, 0.1, 0.5], ‘AssetCovar’，... [0.01, -0.010, 0.004; -0.010, 0.040, -0.002; 0.004, -0.002, 0.023]);
p = setDefaultConstraints(p);
plotFrontier(p, 20);
weights = estimateMaxSharpeRatio(p);
[risk, ret] = estimatePortMoments(p, weights);
hold on
plot(risk,ret,‘*r’)
p = Portfolio(‘AssetMean’，[0.3, 0.1, 0.5], ‘AssetCovar’，... [0.01, -0.010, 0.004; -0.010, 0.040, -0.002; 0.004, -0.002, 0.023]);
p = setDefaultConstraints(p);
plotFrontier(p, 20);
p = setSolver(p,‘quadprog’，‘Display’，‘off’，‘ConstraintTolerance’，1.0e-8,‘OptimalityTolerance’，1.0e-8,‘StepTolerance’，1.0e-8,‘MaxIterations’，10000);
weights = estimateMaxSharpeRatio(p);
[risk, ret] = estimatePortMoments(p, weights);
hold on
plot(risk,ret,‘*r’)
```
= Portfolio('AssetMean',[0.3, 0.1, 0.5], 'AssetCovar',[0.01, -0.010, 0.004; -0.010, 0.040, -0.002; 0.004, -0.002, 0.023],'lb', 0,'budget', 1);
plotFrontier(p, 20);
p = setSolver(p, 'fmincon', 'Display', 'off', 'Algorithm', 'sqp', ...
'SpecifyObjectiveGradient', true, 'SpecifyConstraintGradient', true, ...
'ConstraintTolerance', 1.0e-8, 'OptimalityTolerance', 1.0e-8, 'StepTolerance', 1.0e-8);
weights = estimateMaxSharpeRatio(p);
te = 0.08;
p = setTrackingError(p,te,weights);
[risk, ret] = estimatePortMoments(p,weights);
hold on plot(risk,ret,'*r');
以下是根据提供的内容完成的内容重构，并保持段落结构：
首先，我们定义了蓝筹股的瞬间值、市场风险和现金风险：
```matlab
mret = MarketMean;
mrsk = sqrt(MarketVar);
cret = CashMean;
crsk = sqrt(CashVar);
```
接着，我们计算投资组合的初始价值和风险：
```matlab
p = Portfolio('AssetList', AssetList, 'RiskFreeRate', CashMean);
p = setAssetMoments(p, AssetMean, AssetCovar);
p = setInitPort(p, 1/p.NumAssets);
[ersk, eret] = estimatePortMoments(p, p.InitPort);
```
然后，我们为投资组合设置默认约束条件：
```matlab
p = setDefaultConstraints(p);
```
接下来，我们估算投资组合的前沿权重：
```matlab
pwgt = estimateFrontier(p, 20);
[prsk, pret] = estimatePortMoments(p, pwgt);
```
然后，我们计算完全优化的投资组合的风险-收益比：
```matlab
pwgtshpr_fully = estimateMaxSharpeRatio(p,'Method','direct');
[riskshpr_fully, retshpr_fully] = estimatePortMoments(p,pwgtshpr_fully);
```
最后，我们设定投资组合的时间范围和目标权重，以及估算时间范围内的投资组合权重和收益：
```matlab
q = setBudget(p, 0, 1);
qwgt = estimateFrontier(q, 20);
[qrsk, qret] = estimatePortMoments(q, qwgt);
```
根据提供的内容，以下是重构后的代码：
```matlab
pwgtshpr_direct = estimateMaxSharpeRatio(q, 'Method', 'direct');
pwgtshpr_iter = estimateMaxSharpeRatio(q, 'Method', 'iterative'); % Default for 'TolX' is 1e-8
[riskshpr_diret, retshpr_diret] = estimatePortMoments(q, pwgtshpr_direct);
[riskshpr_iter, retshpr_iter] = estimatePortMoments(q, pwgtshpr_iter);
clf;
portfolioexamples_plot('Efficient Frontier with Capital Allocation Line', ...
{'line', prsk, pret, {'EF'}, '-r', 2}, ...
{'line', qrsk, qret, {'EF with riskfree'}, '-b', 1}, ...
{'scatter', [mrsk, crsk, ersk, riskshpr_fully, riskshpr_diret, riskshpr_iter], ...
[mret, cret, eret, retshpr_fully , retshpr_diret, retshpr_iter], {'Market', 'Cash', 'Equal', 'Sharpe fully invest', 'Sharpe diret', 'Sharpe iter'}}, ...
{'scatter', sqrt(diag(p.AssetCovar)), p.AssetMean, p.AssetList, '.r'});
```
重构后的代码与原始代码相同，只是将一些缩进进行了调整以提高代码的可读性。
请将以下代码进行重构并保持段落结构：
```matlab
maxReturn = max(qret); % 最大收益组合
minReturn = min(qret); % 最小收益组合
display(0.01 * (maxReturn - minReturn));
pwgtshpr_iter_largerTol = estimateMaxSharpeRatio(q, 'Method', 'iterative', 'TolX', 1e-4);
display(table(pwgtshpr_iter, pwgtshpr_iter_largerTol, 'VariableNames', {'Default TolX = 1e-8', 'TolX = 1e-4'}));
```
重构后的代码如下：
```matlab
% 计算最大收益和最小收益组合
maxReturn = max(qret); % 最大收益组合
minReturn = min(qret); % 最小收益组合
% 显示最大和最小收益之间的百分比差异
display(0.01 * (maxReturn - minReturn));
% 使用迭代方法估算最大夏普比率，并设置容差值为1e-4
pwgtshpr_iter_largerTol = estimateMaxSharpeRatio(q, 'Method', 'iterative', 'TolX', 1e-4);
% 显示迭代结果和变量名称
display(table(pwgtshpr_iter, pwgtshpr_iter_largerTol, 'VariableNames', {'默认容差值 TolX = 1e-8', '容差值 TolX = 1e-4'}));
```
序号 | ToleranceX_1e-8 | ToleranceX_1e-4 | 数值 |
|----|--------------|---------------|------------|
| 0 | 3.07295e-09 | 3.11629e-05 | 0.009173 |
| 1 | 3.07295e-09 | 3.11629e-05 | 0.009174 |
| 2 | 3.14526e-08 | 3.18489e-04 | 0.031008 |
| 3 | 3.14526e-08 | 3.18489e-04 | 0.031011 |
| 4 | 5.47781e-16 | 5.66877e-07 | 0 |
| 5 | 5.47781e-16 | 5.66877e-07 | 0 |
| 6 | 3.24272e-07 | 3.32224e-05 | 0 |
| 7 | 3.24272e-07 | 3.32224e-05 | 0 |
| 8 | 4.86949e-07 | 4.99289e-05 | 0 |
| 9 | 4.86949e-07 | 4.99289e-05 | 0 |
| 10 | 5.79588e-16 | 6.02465e-07 | 0 |
| 11 | 5.79588e-16 | 6.02465e-07 | 0 |
| 12 | 1.59638e-05 | 1.67345e-04 | ... |
| ...| ... | ... | ... |
AssetMean = [ 00101110; 0.0043532; 0.0137058]
AssetCovar = [ 0.00324625; 0.00022983; 0.00420395].
p = Portfolio('AssetMean', AssetMean, 'AssetCovar', AssetCovar).
p = setDefaultConstraints(p).
p = setBounds(p, 0.02, 0.5,'BoundType', 'Conditional', 'NumAssets', 3).
p = setMinMaxNumAssets(p, 2, 2).
weights = estimateMaxSharpeRatio(p,'Method','iterative')
以下是重构后的内容：
MaxIterations: 1000
AbsoluteGapTolerance: 1.0000e-07
RelativeGapTolerance: 1.0000e-05
NonlinearScalingFactor: 1000
ObjectiveScalingFactor: 1000
Display: 'off'
CutGeneration: 'basic'
MaxIterationsInactiveCut: 30
ActiveCutTolerance: 1.0000e-07
IntMainSolverOptions: [1x1 optim.options.Intlinprog]
NumIterationsEarlyIntegerConvergence: 30
ExtendedFormulation: 0
NumInnerCuts: 10
NumInitialOuterCuts: 10
