660 Optimizing Eigenvalues of Symmetric Definite Pencils
J.P. Haeberly, M. Overton
We consider the following quasiconvex optimization problem: minimize the largest eigenvalue of a symmetric definite matrix pencil depending on parameters. A new form of optimality conditions is given, emphasizing a complementarity condition on primal and dual matrices. Newton's method is then applied to these conditions to give a new quadratically convergent interior-point method which works well in practice. The algorithm is closely related to primal-dual interior-point methods for semidefinite programming.