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By transforming the constraints containing fuzzy variables into their equivalent forms, the chance constraints are converted into deterministic constraints which consider the fuzzy risks (or reliability) as well.
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Moreover, some global optimality conditions for nonlinear problems with box constraints are presented by transforming the box constraints into n quadratic inequality constraints.
By transforming the complementarity constraints into a fixed point equation with projection type, we propose an l 1 regularization projection minimization model for relaxation.
By transforming the fuzzy chance constraints into their equivalent forms, traditional optimization algorithms can be used to solve the optimal scheduling problem.
This study converts the GFMOOP into a fuzzy multi-objective optimization problem with ε-constraints, abbreviated as ε-FMOOP, by transforming the crisp objective function to an ε-constraint.
It transforms itself by transforming the world.
The combinatorial conditions for hybrid distance-bearing information were derived by transforming bearing constraints into direction constraints which were studied in discrete geometry literature.
Furthermore, the problem is reformulated by introducing an extra decision vector to transform the original constraints into linear constraints, and then, a two-phase method is presented to solve the problem efficiently.
Through transforming the objectives and the constraints step by step, all proposed constraints are satisfied very well.
Using a minimax optimization approach, the sum-capacity for the MIMO-BCs has been studied in [25, 26], which seeks to maximize the minimal SNR among all the users under the sum-power constraint by transforming into its dual MAC problem.
Then, we consider robust secure transmit scheme design where the CSI uncertainties are molded by the worst-case model, and S-procedure and its extension are employed to transform the semi-infinite constraint problem into finite constraint problem.
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