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I'm not that familiar with MATLAB. However, in quadratic programming the main issue I've found is setting up the problem correctly and then the coding becomes much easier. As you noted this problem can be expressed as a quadratic cone problem and solved by quadprog but a good amount of more work needs to be done to get this in the correct form. You ...


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The mean-variance portfolio is given by $\sigma_t^{-1} \theta_t$ where $\theta_t$ is the market price of risk $\frac{\mu_t - r_t}{\sigma_t}$. Here your taking $\Sigma^{-1} (\mu_t - r_t)$ and using block matrices to enforce the constraints as outlined by Arrigo.


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From how you outlined your solution, you are computing the mean variance portfolio with minimum risk and with target return $\overline{r}$. I'd say that you are solving an optimization using Lagrange multiplier method given the values of matrix A. $\lambda$ and $\mu$ are the Lagrande multipliers: these parameters measure the sensitivity of the Lagrange ...



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