A primal-dual penalty method via rounded weighted-ℓ1 Lagrangian duality
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2022
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Burachik, R.S.
Kaya, C.Y.
Price, C.J.
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Optimization, 2022; 71(13):3981-4017
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We propose a new duality scheme based on a sequence of smooth minorants of the weighted-l1 penalty func- tion, interpreted as a parametrized sequence of augmented Lagrangians, to solve non-convex constrained optimization problems. For the induced sequence of dual problems, we establish strong asymptotic duality properties. Namely, we show that (i) the sequence of dual problems is convex and (ii) the dual values monotonically increase to the optimal primal value. We use these properties to devise a subgradient based primal–dual method, and show that the generated primal sequence accumulates at a solution of the original problem. We illustrate the performance of the new method with three different types of test problems: A polynomial non-convex problem, large-scale instances of the celebrated kissing num- ber problem, and the Markov–Dubins problem. Our numerical experiments demonstrate that, when compared with the tra- ditional implementation of a well-known smooth solver, our new method (using the same well-known solver in its sub- problem) can find better quality solutions, i.e. ‘deeper’ local minima, or solutions closer to the global minimum. Moreover, our method seems to be more time efficient, especially when the problem has a large number of constraints.
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Copyright 2021 Informa
Access Condition Notes: Accepted manuscript available after 1 July 2022