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Thus, the design was formulated as a mixed-integer nonlinear optimization problem.
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The design is formulated as a constrained nonlinear programming problem.
The design is formulated using the least number of terms in each design equation, thus providing the most robust set of equations for numerical solutions.
The design is formulated through LMI (Linear Matrix Inequalities) techniques under equality constraints.
In [9], the design is formulated as a sampled-data optimization problem (suppose the primal filters are known) and then solved by standard approximation theory.
The design is formulated as a nonlinear programming problem including the differential equations that model the mass transfer in the membrane modules as equality constraints.
The design is formulated in Linear Matrix Inequality (LMI) framework, such that the optimal solution of the stated control problem is obtained.
The design is formulated as a nonlinear programming problem where the set of algebraic and differential equations that model the membrane separation processes are included as equality constraints.
The design is formulated as a Linear Matrix Inequality (LMI) problem, the solution of which returns a fixed structure delay-dependent robust LFC.
The design is formulated as a multi-constrained optimization problem with a least square objective function to minimize structural shape errors.
In first instance, the design is formulated as a multicriteria optimization problem with a number of conflicting objectives and then a multiobjective optimization strategy is implemented to find the Pareto optimal set of solutions.
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