A nonsingular matrix is a square matrix that has a unique inverse. This means that the matrix can be multiplied with its inverse to give the identity matrix (a matrix with 1s on the diagonal and 0s elsewhere).
Formulating nonsingularity of matrix (I + A*B) in LMIs (Linear Matrix Inequalities) is important in control theory and optimization problems. It allows for the use of convex optimization techniques to find solutions, which are more efficient and reliable compared to traditional methods.
A matrix can be checked for nonsingularity by calculating its determinant. If the determinant is equal to 0, then the matrix is singular. Alternatively, the rank of the matrix can also be used to determine its nonsingularity - a matrix with full rank is nonsingular.
The formula for formulating nonsingularity of matrix (I + A*B) in LMIs is: (I + A*B) > 0, where > 0 indicates that the matrix is positive definite.
Yes, nonsingularity of matrix (I + A*B) can be formulated for any values of A and B as long as the dimensions of the matrices are compatible for multiplication and the matrices are square. However, the formulation may not always have a feasible solution, depending on the values of A and B.