SDP problems arise from the well-known linear programming problems by replacing the vector of variables with a symmetric matrix
and replacing the non-negativity constraints with positive semidefinite constraints.
If x = y, then we observe that M is a symmetric matrix
1[less than or equal to]i,j[less than or equal to]N] be a skew symmetric matrix
of size N.
It is well known that since A is a real symmetric matrix
all its eigen values must be real.
These condition are described in terms of certain symmetric matrix
Where CA is an asymmetric matrix, CS is a symmetric matrix
, and CTA is the transposed asymmetric matrix.
Where the elements of the (5x5) symmetric matrix
[summation](M) are given by Cov([m.
L) are weights, and [S] is a symmetric matrix
Of course, the functional G would not exist if A was not a symmetric matrix
The characteristic polynomial for the symmetric matrix
The shortest path distance matrix is represented as square symmetric matrix
of size n x n, where n is the number of links in a KC.
The Lanczos algorithm is one of the most frequently used to compute a few eigenvalues of a large sparse symmetric matrix
, but it does not fulfill its theoretical potential when used in finite precision arithmetic.