# 10.5 Nearest Correlation Matrix Problem¶

A correlation matrix is a symmetric positive definite matrix with unit diagonal. This term has origins in statistics, since the matrix whose entries are the correlation coefficients of a sequence of random variables has all these properties.

In this section we study variants of the problem of approximating a given symmetric matrix $$A$$ with correlation matrices:

• find the correlation matrix $$X$$ nearest to $$A$$ in the Frobenius norm,
• find an approximation of the form $$D+X$$ where $$D$$ is a diagonal matrix with positive diagonal and $$X$$ is a positive semidefinite matrix of low rank, using the combination of Frobenius and nuclear norm.

Both problems are related to portfolio optimization, where one can often have a matrix $$A$$ that only approximates the correlations of stocks. For subsequent optimizations one would like to approximate $$A$$ with a correlation matrix or, in the factor model, with $$D+VV^T$$ with $$VV^T$$ of small rank.

## 10.5.1 Nearest correlation with the Frobenius norm¶

The Frobenius norm of a real matrix $$M$$ is defined as

$\|M\|_F = \left(\sum_{i,j}M_{i,j}^2\right)^{1/2}$

and with respect to this norm our optimization problem can be expressed simply as:

(1)$\begin{split}\begin{array}{ll} \minimize & \|A-X\|_F\\ \st & \mathbf{diag}(X) = e,\\ & X \succeq 0.\\ \end{array}\end{split}$

We can exploit the symmetry of $$A$$ and $$X$$ to get a compact vector representation. To this end we make use of the following mapping from a symmetric matrix to a flattened vector containing the (scaled) lower triangular part of the matrix:

(2)$\begin{split}\begin{array}{ll} \mbox{vec}: & \R^{n\times n} \rightarrow \real^{n(n+1)/2} \\ \mbox{vec}(M) = & (\alpha_{11}M_{11},\alpha_{21}M_{21},\alpha_{22}M_{22},\ldots,\alpha_{n1}M_{n1},\ldots,\alpha_{nn}M_{nn}) \\ \alpha_{ij}=&\begin{cases}1 & j=i\\ \sqrt{2} & j<i\end{cases} \end{array}\end{split}$

Note that $$\|M\|_F=\|\mbox{vec}(M)\|_2$$. The Fusion implementation of $$\mbox{vec}$$ is as follows:

Listing 26 Implementation of function $$vec$$ in (2). Click here to download.
  /** Assuming that e is an NxN expression, return the lower triangular part as a vector.
*/
public static Expression vec(Expression e) {
int N         = e.getShape().dim(0);
int[] msubi   = new int[N * (N + 1) / 2];
int[] msubj   = new int[N * (N + 1) / 2];
double[] mcof = new double[N * (N + 1) / 2];

for (int i = 0, k = 0; i < N; ++i)
for (int j = 0; j < i + 1; ++j, ++k) {
msubi[k] = k;
msubj[k] = i * N + j;
if (i == j) mcof[k] = 1.0;
else        mcof[k] = Math.sqrt(2);
}

Matrix S = Matrix.sparse(N * (N + 1) / 2, N * N, msubi, msubj, mcof);
return Expr.mul(S, Expr.flatten(e));
}


That leads to an optimization problem with both conic quadratic and semidefinite constraints:

(3)$\begin{split}\begin{array}{ll} \minimize & t\\ \st & (t, \mbox{vec} (A-X)) \in \Q,\\ & \mathbf{diag}(X) = e,\\ & X \succeq 0.\\ \end{array}\end{split}$

Code example

Listing 27 Implementation of problem (3). Click here to download.
  private static void nearestcorr(Matrix A)
throws SolutionError {
int N = A.numRows();

Model M = new Model("NearestCorrelation");
try {
// Setting up the variables
Variable X = M.variable("X", Domain.inPSDCone(N));
Variable t = M.variable("t", 1, Domain.unbounded());

// (t, vec (A-X)) \in Q
M.constraint( Expr.vstack(t, vec(Expr.sub( A, X))), Domain.inQCone() );

// diag(X) = e
M.constraint(X.diag(), Domain.equalsTo(1.0));

// Objective: Minimize t
M.objective(ObjectiveSense.Minimize, t);

// Solve the problem
M.solve();

// Get the solution values
System.out.println("X = \n" + mattostr(X.level(), N));
System.out.println("t = \n" + mattostr(t.level(), N));

} finally {
M.dispose();
}
}


We use the following input

Listing 28 Input for the nearest correlation problem.
    int N = 5;
Matrix A = Matrix.dense(N, N,
new double[] { 0.0,  0.5,  -0.1,  -0.2,   0.5,
0.5,  1.25, -0.05, -0.1,   0.25,
-0.1, -0.05,  0.51,  0.02, -0.05,
-0.2, -0.1,   0.02,  0.54, -0.1,
0.5,  0.25, -0.05, -0.1,   1.25
});


The expected output is the following (small differences may apply):

X =
[ 1.000e+00  5.000e-01  -1.000e-01  -2.000e-01  5.000e-01 ]
[ 5.000e-01  1.000e+00  -5.000e-02  -9.999e-02  2.500e-01 ]
[ -1.000e-01  -5.000e-02  1.000e+00  2.000e-02  -5.000e-02 ]
[ -2.000e-01  -9.999e-02  2.000e-02  1.000e+00  -9.999e-02 ]
[ 5.000e-01  2.500e-01  -5.000e-02  -9.999e-02  1.000e+00 ]


## 10.5.2 Nearest Correlation with Nuclear-norm Penalty¶

Next, we consider the approximation of $$A$$ of the form $$D+X$$ where $$D=\diag(w),\ w\geq 0$$ and $$X\succeq 0$$. We will also aim at minimizing the rank of $$X$$. This can be approximated by a relaxed linear objective penalizing the trace $$\trace(X)$$ (which in this case is the nuclear norm of $$X$$ and happens to be the sum of its eigenvalues).

The combination of these constraints leads to a problem:

$\begin{split}\begin{array}{ll} \minimize & \left\|X+\diag(w)-A\right\|_F + \gamma \trace(X),\\ \st & X \succeq 0, w \geq 0, \end{array}\end{split}$

where the parameter $$\gamma$$ controls the tradeoff between the quality of approximation and the rank of $$X$$.

Exploit the mapping $$\mbox{vec}$$ defined in (2) we can express this problem as:

(4)$\begin{split}\begin{array}{ll} \minimize & t + \gamma\trace(X) \\ \st & (t, \mbox{vec} (X + \diag(w) - A) ) \in \Q, \\ & X \succeq 0 , w \geq 0. \end{array}\end{split}$

Code example

Listing 29 Implementation of problem (4). Click here to download.
  /* Nearest correlation with nuclear norm penalty */
private static void nearestcorr_nn(Matrix A, double[] gammas, double[] res, int[] rank)
throws SolutionError {
int N = A.numRows();
Model M = new Model("NucNorm");
try {
// Setup variables
Variable t = M.variable("t", 1, Domain.unbounded());
Variable X = M.variable("X", N, Domain.inPSDCone());
Variable w = M.variable("w", N, Domain.greaterThan(0.0));

// (t, vec (X + diag(w) - A)) in Q
Expression D = Expr.mulElm( Matrix.eye(N), Var.repeat(w, 1, N) );
M.constraint( Expr.vstack( t, vec(Expr.sub(Expr.add(X, D), A)) ), Domain.inQCone() );

// Trace(X)
Expression TX = Expr.sum(X.diag());

for (int k = 0; k < gammas.length; ++k) {
// Objective: Minimize t + gamma*Tr(X)
M.solve();

// Get the eigenvalues of X and approximate its rank
double[] d = new double[N];
LinAlg.syeig(mosek.uplo.lo, N, X.level(), d);
int rnk = 0; for (int i = 0; i < d.length; ++i) if (d[i] > 1e-6) ++rnk;

res[k] = t.level()[0];
rank[k] = rnk;
}
} finally {
M.dispose();
}
}


We feed MOSEK with the same input as in Sec. 10.5.1 (Nearest correlation with the Frobenius norm). The problem is solved for a range of values $$\gamma$$ values, to demonstrate how the penalty term helps achieve a low rank solution. To this extent we report both the rank of $$X$$ and the residual norm $$\left\|X+\diag(w)-A\right\|_F$$.

gamma = 0.0, res=3.076e-01, rank = 4
gamma = 0.1, res=4.252e-01, rank = 2
gamma = 0.2, res=5.112e-01, rank = 1
gamma = 0.3, res=5.298e-01, rank = 1
gamma = 0.4, res=5.593e-01, rank = 1
gamma = 0.5, res=6.046e-01, rank = 1
gamma = 0.6, res=6.764e-01, rank = 1
gamma = 0.7, res=8.010e-01, rank = 1
gamma = 0.8, res=1.062e+00, rank = 1
gamma = 0.9, res=1.130e+00, rank = 0
gamma = 1.0, res=1.130e+00, rank = 0