When applied to Lagrangian relaxation of nonconvex programs, they find solutions to relaxed convexified versions of such programs. Numerical results are ...

When applied to Lagrangian relaxation of nonconvex programs, they nd solutions to relaxed convexi ed versions of such programs. Nu- merical results are ...

Abstract. We exhibit useful properties of proximal bundle methods for finding mins f, where f and S are convex. We show that they asymptotically find ...

When applied to Lagrangian relaxation of nonconvex programs, they find solutions to relaxed convexified versions of such programs. Numerical results are ...

When applied to Lagrangian relaxation of nonconvex programs, they find solutions to relaxed convexified versions of such programs. Numerical results are ...

Dual Applications of Proximal Bundle Methods, Including Lagrangian Relaxation of Nonconvex Problems ; Proximal Bundle Methods in Unit Commitment Optimization.

Bundle methods have been used to maximize the nonsmooth dual function in Lagrangian relaxation for integer optimization problems, where the subgradients are ...

Bundle methods have been used to maximize the nonsmooth dual function in Lagrangian relax- ation for integer optimization problems, where the subgradients are ...

Based on augmented Lagrangian, we propose in this paper a new dual for inequality constrained nonconvex generalized fractional programs (GFP).

Dual Applications of Proximal Bundle Methods, Including Lagrangian Relaxation of Nonconvex Problems · A Proximal-Projection Bundle Method for Lagrangian ...