An equivalent nonlinear optimization model with triangular low-rank factorization for semidefinite programs

In this paper, we propose a new nonlinear optimization model to solve semidefinite optimization problems (SDPs), providing some properties related to local optimal solutions. The proposed model is based on another nonlinear optimization model given by [S. Burer and R. Monteiro, A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization, Math. Program. Ser. B 95 (2003), pp. 329-357], but it has several nice properties not seen in the existing one. Firstly, the decision variable of the proposed model is a triangular low-rank matrix. Secondly, the existence of a strict local optimum of the proposed model is guaranteed under some conditions, whereas the existing model has no strict local optimum. In other words, it is difficult to construct solution methods equipped with fast convergence using the existing model. We also present some numerical results, showing that the use of the proposed model allows to deliver highly accurate solutions.

Automated summary

In this paper, a new nonlinear optimization model is proposed to solve semidefinite optimization problems (SDPs), and some properties related to local optimal solutions are provided. The proposed model is based on another nonlinear optimization model, but it has several nice properties not seen in the existing one. The decision variable of the proposed model is a triangular low-rank matrix, and the existence of a strict local optimum is guaranteed.