Solving Semidefinite Programs via Nonlinear Programming Part I : Transformations and Derivatives ∗

@inproceedings{Burer1999SolvingSP,
  title={Solving Semidefinite Programs via Nonlinear Programming Part I : Transformations and Derivatives ∗},
  author={Samuel Burer and Renato D. C. Monteiro and Yin Zhang},
  year={1999}
}
In this paper, we introduce a transformation that converts a class of linear and nonlinear semidefinite programming (SDP) problems into nonlinear optimization problems over “orthants” of the form <++ × <N , where n is the size of the matrices involved in the problem and N is a nonnegative integer dependent upon the specific problem. For example, in the case of the SDP relaxation of a MAXCUT problem, n is the number of vertices in the underlying graph and N is zero. The class of transformable… CONTINUE READING
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