This paper studies an alternative technique to interior point methods and nonlinear methods for semidefinite programming (SDP). The approach based on classical quadratic regularizations leads to an algorithm, generalizing a recent method called "boundary point method". We study the theoretical properties of this algorithm and we show that in practice it behaves very well on some instances of SDP having a large number of constraints.
J. Malick, J. Povh, F. Rendl, and A. Wiegele. Regularization methods for semidenite programming. SIAM J. Optim., 20(1):336-356, 2009