MIQP for portfolio optimization
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I have to find the optimal portfolio weights for a set of K, where k is a sort of cardinality of my portfolio. X is a continuous variable, while y is a binary one. The real problem is that the script below doesn't work (I think for the LB and UB)
Q=[sigma zeros(n);zeros(n,2*n)];
Aeq=[ones(1,n) zeros(1,n); zeros(1,n) ones(1,n)];
K=5
model.Q=sparse(Q);
model.A=sparse(Aeq);
model.obj=zeros(2*n,1);
model.rhs=[1;K];
model.sense=['=';'<'];
model.lb=[zeros(1,n) zeros(1,n)];
model.ub=[ones(1,n) ones(1,n)];
model.vtype=[repmat('C',1,n) ; repmat('B',1,n)];
Obviously the first row of the Aeq matrix is deleted (expected return constraint) cause I'm using the epsilon-constraints method
P.S I'm using gurobi in the Matlab interface
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The constraints \( \ell_i y_i \leq x_i \leq u_i y_i,\ i = 1, \ldots, n \) can't be modeled as simple bound constraints, because the bounds are not constants. These constraints should be incorporated into the constraint matrix as
$$\begin{alignat*}{2} -x_i + \ell_i y_i &\leq 0 \quad && i = 1, \ldots, n \\ x_i - u_i y_i &\leq 0 && i = 1, \ldots, n.\end{alignat*}$$
If you're only trying to add \( [0, 1] \) bounds to your variables for now, that is implemented correctly.
The \( \texttt{vtype} \) field should be a one-dimensional vector of length \( 2n \). It is currently defined as a \( 2 \times n \) \( \texttt{char} \) array:
>> [repmat('C',1,n) ; repmat('B',1,n)]
ans =
2×10 char array
'CCCCCCCCCC'
'BBBBBBBBBB'This will cause the variables types to not match what you intend. Instead, try:
model.vtype = [repmat('C',n,1); repmat('B',n,1)];
Note that you can use the gurobi_write() function to save the model as an LP file for visual inspection:
gurobi_write(model, 'model.lp');
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I've modified my model, but probably I wrote in a wrong way this new constraints
I 've used the vector [-eye(n) zeros(1,n)*eye(n) ; eye(n) -(ones(1,n)*eye(n) ]
while for the lower and upper bounds, respectively zeros(2*n,1) and ones(2*n,1)
Something is wrong
P.S thanks for your help, is the first time for me in this kind of optimization using Gurobi
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The constraints
$$\begin{alignat*}{2} -x_i + \ell_i y_i &\leq 0 \quad && i = 1, \ldots, n \\ x_i - u_i y_i &\leq 0 && i = 1, \ldots, n.\end{alignat*}$$
can be equivalently written as
$$\begin{align*}\begin{bmatrix}-I &\phantom{-}L\\ \phantom{-}I &-U\end{bmatrix} \begin{bmatrix}x \\ y\end{bmatrix} &\leq \begin{bmatrix} {\bf{0}} \\ {\bf{0}} \end{bmatrix},\end{align*}$$
where \( I \in \mathbb{R}^{n \times n} \) is the identity matrix, \( L \in \mathbb{R}^{n \times n} \) is the diagonal matrix formed by the \( \ell_i \), \( U \in \mathbb{R}^{n \times n} \) is the diagonal matrix formed by the \( u_i \), and \( {\bf{0}} \in \mathbb{R}^n \) is the vector of zeros.
You can add these constraints to the model by modifying the \( \texttt{A} \), \( \texttt{sense} \), and \( \texttt{rhs} \) fields of your \( \texttt{model} \) \( \texttt{struct} \). Let's assume \( \ell_i = 0 \) and \( u_i = 1 \) for all \( i = 1, \ldots, n \). Then \( L \) is the matrix of zeros and \( U \) is the identity matrix:
Aeq = [ones(1,n), zeros(1,n); zeros(1,n), ones(1,n); -eye(n), zeros(n); eye(n), -eye(n)];
model.A = sparse(Aeq);
model.rhs = [1; K; zeros(2*n,1)];
model.sense = ['='; repmat('<',2*n+1,1)];In this case, because \( \ell_i = 0 \) for all \( i = 1, \ldots, n \) and \( x \) is nonnegative, the constraints \( -x_i + \ell_i y_i \leq 0 \) are redundant. Thus, you can skip these constraints:
Aeq = [ones(1,n), zeros(1,n); zeros(1,n), ones(1,n); eye(n), -eye(n)];
model.A = sparse(Aeq);
model.rhs = [1; K; zeros(n,1)];
model.sense = ['='; repmat('<',n+1,1)];0 -
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