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Furthermore, several new equivalent optimality conditions for optimization problems with inequality constraints are obtained.
Jiang [13] studies the optimality conditions for optimization problems with second-order cone equilibrium constraints, the Aubin property of the second-order cone complementarity set and the smoothing methods for solving inverse linear programming problems and inverse linear second-order cone programming problems.
Therefore, the Karush-Kuhn-Tucker (KKT) conditions for optimization are sufficient and necessary for the optimality [5].
It was also found that well transmissibility is a vital factor to satisfy desired conditions for optimization process.
The conditions for optimization: begin{array}{*{20}l} palpha kS^{alpha-1}I^{beta} P^{gamma} N^{delta} =w_{1} end{array} (42).
The adaptive ADCs employ simple internal digital processing units (DPUs) to compute the output codes, which creates the conditions for optimization of the conversion algorithm and improvement of their effective number of bits (ENOB) beyond ENOB of their conventional counterparts.
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On the other hand, it can be demonstrated that this design condition for optimization has never been studied in the literature of fin heat transfer before.
At the same time, second-order optimality conditions and higher-order optimality conditions for vector optimization problems have been extensively studied in the literature (see [3 18]).
The Kuhn-Tucker (KT) equations are the necessary conditions for optimality for a constrained optimization problem.
Jimenez and Novo [7, 8] obtained first- and second-order optimality conditions for vector optimization problems.
We give an application to derive asymptotic optimality conditions for convex optimization.
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