A smoothing augmented Lagrangian method for solving simple bilevel programs

Mengwei Xu () and Jane Ye ()

Computational Optimization and Applications, 2014, vol. 59, issue 1, 353-377

Abstract: In this paper, we design a numerical algorithm for solving a simple bilevel program where the lower level program is a nonconvex minimization problem with a convex set constraint. We propose to solve a combined problem where the first order condition and the value function are both present in the constraints. Since the value function is in general nonsmooth, the combined problem is in general a nonsmooth and nonconvex optimization problem. We propose a smoothing augmented Lagrangian method for solving a general class of nonsmooth and nonconvex constrained optimization problems. We show that, if the sequence of penalty parameters is bounded, then any accumulation point is a Karush-Kuch-Tucker (KKT) point of the nonsmooth optimization problem. The smoothing augmented Lagrangian method is used to solve the combined problem. Numerical experiments show that the algorithm is efficient for solving the simple bilevel program. Copyright Springer Science+Business Media New York 2014

Keywords: Bilevel program; Value function; Smoothing method; Augmented Lagrangian method; Partial calmness; Principal-agent problem (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10589-013-9627-7

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