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- W2460172031 abstract "In this paper we investigate how to efficiently apply Approximate-Karush–Kuhn–Tucker proximity measures as stopping criteria for optimization algorithms that do not generate approximations to Lagrange multipliers. We prove that the KKT error measurement tends to zero when approaching a solution and we develop a simple model to compute the KKT error measure requiring only the solution of a non-negative linear least squares problem. Our numerical experiments on a Genetic Algorithm show the efficiency of the strategy." @default.
- W2460172031 created "2016-07-22" @default.
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- W2460172031 date "2015-09-01" @default.
- W2460172031 modified "2023-10-13" @default.
- W2460172031 title "Convergence detection for optimization algorithms: Approximate-KKT stopping criterion when Lagrange multipliers are not available" @default.
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- W2460172031 doi "https://doi.org/10.1016/j.orl.2015.06.009" @default.
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