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To solve the non-convex optimization problem under consideration, first, we transform the primal optimization problem in fractional form into a tractable parameterized subtractive form optimization problem.
The results in ([17], Theorem 1) has revealed that there exists an equivalent optimization problem with an objective function in subtractive form for an optimization problem with an objective function in fractional form.
The considered optimization is non-convex and hard to tackle; to solve it, we first transform the original objective function in fractional form into a parameterized subtractive form optimization problem [18,19].
Multilevel fractional programming problems (MLFPPs) involve objective functions in fractional form, i.e., f ( X ) = N ( X ) D ( X ) Open image in new window at each level with the assumption that the denominator of objectives remains positive at each level in the feasible region.
Refer to Appendix 3. Theorem 3 shows that the transformed problem with an objective function in subtractive form is equivalent to the non-convex problem (P2-1) in fractional form, i.e., they lead to the same optimum solution P s * and P d *.
Data is presented in fractional form as survivors over total patients within each category.
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Write each ratio in its fractional form, then set the two ratios equal to each other and cross multiply to solve.
The uncertainties of the systems under consideration are expressed in a linear fractional form.
The wage component appears as a fractional change relative to the prior year, while the equipment component is a difference of two percentages which must be divided by 100 to present it in a consistent fractional form.
Moreover, the fractional form in the objective function (27) makes the problem more intractable.
Two notable examples of reaction-diffusion systems taken from the literature are considered and formulated in space-fractional form.
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