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0. Each iteration involves solving an unconstrained proximal subproblem and projecting a certain point onto C. The method asymptotically finds points that are ε-optimal. In Lagrangian relaxation of convex programs, it allows for ε-accurate solutions of Lagrangian subproblems and finds ε-optimal primal solutions. For semidefinite programming problems, it extends the highly successful spectral bundle method to the case of inexact eigenvalue computations.]]> 0. Each iteration involves solving an unconstrained proximal subproblem and projecting a certain point onto C. The method asymptotically finds points that are ε-optimal. In Lagrangian relaxation of convex programs, it allows for ε-accurate solutions of Lagrangian subproblems and finds ε-optimal primal solutions. For semidefinite programming problems, it extends the highly successful spectral bundle method to the case of inexact eigenvalue computations.]]>