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We introduce a novel random gradient bundle method for approximating local minimizers, motivated by recent work on non-smooth analysis of the function α(X).
Since the bi-level signal control problem is generally a non-convex program, a bundle method using generalized gradients is proposed.
Then the limited memory bundle method [Haarala et.al. Math. Prog., Vol. 109, No. 1, pp. 181 205, 2007] is modified and combined with an incremental approach to design a new clustering algorithm.
We solve the dual problem with a bundle method.
3, we present the alternating linearization bundle method for problem (1).
We analyze the resulting dynamic bundle method giving a positive answer for its primal-dual convergence properties, and, under suitable conditions, show finite termination for polyhedral problems.
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Includes batch and incremental subgradient, cutting plane, proximal, and bundle methods.
We extend the concept of bundle methods to address non-convex and non-smooth optimization problems arising in the design of a feedback control law for the longitudinal flight control of a civil aircraft.
The bundle methods and extragradient methods were extended to equilibrium problems in [6] and [7].
Our purpose is twofold: first, bundle methods are the methods of choice in nonsmooth optimization when it comes to solve large-scale problems with high precision.
As usual in bundle methods, two cases are considered: the algorithm generates finite number of descent steps; and the algorithm generates infinite number of descent steps.
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