For iterative sequences that converge to the solution set of a linear matrix inequality, we show that the distance of the iterates to the solution set is at most O(ffl 2 Gammad ). The nonnegative integer d is the so--called degree of singularity of the linear matrix inequality, and ffl denotes the amount of constraint violation in the iterate. For infeasible linear matrix inequalities, we show
... [Show full abstract] that the minimal norm of ffl--approximate primal solutions is at least 1=O(ffl 1=(2 d Gamma1) ), and the minimal norm of ffl--approximate Farkas-- type dual solutions is at most O(1=ffl 2 d Gamma1 ). As an application of these error bounds, we show that for any bounded sequence of ffl--approximate solutions to a semi-definite programming problem, the distance to the optimal solution set is at most O(ffl 2 Gammak ), where k is the degree of singularity of the optimal solution set. Keywords: semi-definite programming, error bounds, linear matrix inequality, regularized duality. AMS s...