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We solve it by a branch-and-bound algorithm based on Outer Approximation and a heuristic algorithm exploiting the decomposition and reciprocal update of two submodels.
Moreover, we study a method based on outer approximation (OA) to solve the positioning problem for positive measurement errors and propose a new OA method for cooperative networks positioning.
First it will be shown that Decomposition algorithm based on Outer Approximation for solving convex MINLPs and INLPs [5, 6] is a BC algorithm with a certain set of heuristics.
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cc2d2a−/− fish based on outer segment length and rhodopsin mislocalization.
A solution method based on the outer approximation of the nonlinear terms is developed to solve the problem.
In addition, we propose a solution approach based on an outer approximation strategy and show the algorithmic advantage of such framework for this class of programs.
The optimization problem is formulated within the Generalized Disjunctive Programming (GDP) framework and the solution of the reformulated MINLP problem is approached with a decomposition strategy based on the Outer-Approximation algorithm, where NLP subproblems are solved with the derivative free optimizer belonging to the BzzMath library, and MILP master problems are solved with CPLEX/GAMS.
The model is formulated as a Generalized Disjunctive Programming (GDP) problem and solved using the logic based outer approximation algorithm without MINLP reformulation.
Most price indexes are based on some approximation to such a sampling design.
The other approach is based on pair approximation.
The method is based on an approximation result that we called the 'A-caloric approximation lemma'.
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