Convergence detection for optimization algorithms
作者： Gabriel Haeser , Vinícius V. de Melo
DOI: 10.1016/J.ORL.2015.06.009
关键词:
摘要: 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 Lagrange multipliers. We prove the KKT error measurement tends zero when approaching a solution and develop simple model compute measure requiring only of non-negative linear least squares problem. Our numerical experiments on Genetic Algorithm show efficiency strategy.
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