New properties and computational improvement of the GOP algorithm for problems with quadratic objective functions and constraints
作者： V. Visweswaran , C. A. Floudas
DOI: 10.1007/BF01096414
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摘要: In Floudas and Visweswaran (1990, 1993), a deterministic global optimization approach was proposed for solving certain classes of nonconvex problems. An algorithm, GOP, presented the solution problem through series ofprimal andrelaxed dual problems that provide valid upper lower bounds respectively on solution. The algorithm proved to have finite convergence an ∈-global optimum. this paper, new theoretical properties are help enhance computational performance GOP applied special structure. effect is illustrated application difficult indefinite quadratic problem, multiperiod tankage quality occurs frequently in modeling refinery processes, set pooling/blending from literature. addition, extensive experience reported randomly generated concave programming different sizes. results show make computationally efficient fairly large
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