Semidefinite programming for discrete optimization and matrix completion problems
Discrete Applied Mathematics
| Hi-index | 0.00 |
We both characterize and give a convergent algorithm for finding a matrix in a linear variety of matrices that is nearest (in the Frobenius norm) to the positive semidefinite (PSD) matrices. Our motivation is from matrix completions, and in that setting our observations take an especially useful form that we use to bound, and sometimes give closed-form formulae for, the distance from the set of completions to the PSD matrices in terms only of specified data.