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The optimal solution of a geometric program (GP) can be sensitive to variations in the problem data.
An imprecise geometric program is transformed to a family of conventional geometric programs to calculate the objective value.
A geometric program (GP) is a type of mathematical optimization problem characterized by objective and constraint functions that have a special form.
In this paper we apply this method to a specific, widely used operational amplifier architecture, showing in detail how to formulate the design problem as a geometric program.
The resulted geometric program is solved by using the duality approach and the lower and upper bounds are found out for the objective function and variables.
So, the upper and lower bounds are specially formulated for the problem and then, the model is transferred to a geometric program.
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GGPLAB is a Matlab-based toolbox for specifying and solving geometric programs (GPs) and generalized geometric programs (GGPs).
This paper discusses geometric programs with joint probabilistic constraints.
Moreover, goal geometric programming is described incorporating imprecision in it.
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