# Solve Problem for Minimum Tracking Error with Net Return Constraint

This example shows how to use `estimateCustomObjectivePortfolio` to solve a portfolio problem for minimum tracking error with a net return constraint using a custom objective.

### Create `Portfolio` Object

Create a `Portfolio` object.

```% Create a Portfolio object load('SixStocks.mat') p = Portfolio(AssetMean=AssetMean,AssetCovar=AssetCovar);```

### Define Problem

The portfolio problem for a minimum tracking error problem with a net return constraint is defined as

`$\begin{array}{l}\underset{\mathit{x}}{\mathrm{min}}\text{\hspace{0.17em}\hspace{0.17em}}{\left(\mathit{x}-{\mathit{x}}_{0}\right)}^{\mathit{T}}\Sigma \left(\mathit{x}-{\mathit{x}}_{0}\right)\\ \mathit{s}.\mathit{t}.{\mu }^{\mathit{T}}\mathit{x}-{\mathit{c}}_{\mathit{B}}^{\mathit{T}}\mathrm{max}\left(0,\mathit{x}-{\mathit{x}}_{0}\right)-{\mathit{c}}_{\mathit{S}}^{\mathit{T}}\mathrm{max}\left(0,{\mathit{x}}_{0}-\mathit{x}\right)\ge {\mu }_{0}\\ \text{\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}}\sum _{\mathit{i}}{\mathit{x}}_{\mathit{i}}=1\\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\mathit{x}\ge 0\end{array}$`

### Define Problem Parameters

Define the problem parameters for the `Portfolio` object.

```% Initial portfolio initPort = 1/p.NumAssets*ones(p.NumAssets,1); % Buy cost buyCost = 0.001*ones(p.NumAssets,1); % Sell cost sellCost = 0.002*ones(p.NumAssets,1); % Net return target ret0 = 0.03;```

### Solve Portfolio Problem

`Use` `estimateCustomObjectivePortfolio` to solve this portfolio problem for a minimum tracking error with a net return constraint. When using the `estimateCustomObjectivePortfolio` function with a `Portfolio` object, you add return constraints by using the `estimateCustomObjectivePortfolio` name-value argument `TargetReturn` with a return target value.

```% Long-only, fully invested portfolio p = setDefaultConstraints(p); % Set the buy and sell costs p = setCosts(p,buyCost,sellCost,initPort); % Set the objective fun = @(x) (x-initPort)'*p.AssetCovar*(x-initPort); % Solve the portfolio problem wFinTbx = estimateCustomObjectivePortfolio(p,fun,TargetReturn=ret0)```
```wFinTbx = 6×1 0.1633 0.0648 0.1950 0.2618 0.0625 0.2525 ```