The method referred to as generalized program motion equations (GPME) method includes, as special cases, modeling methods based on Lagrange's equations with multipliers, Appell's, Maggi's, or Nielsen's equations.
Across the nation, many generalized programs have focused on retention of minority students in the sciences with varying degrees of success (McElroy and Armesto, 1998).
This paper treats the solution of nonlinear optimization problems involving discrete decision variables, also known as generalized disjunctive programming (GDP) or mixed-integer nonlinear programming (MINLP) problems, that arise in process engineering.
The mathematical model is formulated using generalized disjunctive programming (GDP) and is optimized via mixed-integer nonlinear programming (MINLP), through the minimization of the retrofit total annualized cost, considering the turbine and compressor coupling with a helper motor.
The model is based on a generalized disjunctive programming formulation, which gives rise to a mixed-integer nonlinear programming problem.
The methodology makes use of a generalized disjunctive programming (GDP) formulation which gives rise to a mixed-integer nonlinear programming (MINLP) problem.
GGPLAB is a Matlab-based toolbox for specifying and solving geometric programs (GPs) and generalized geometric programs (GGPs).
The following is a library of examples for GGPLAB, a Matlab-based toolbox for specifying and solving geometric programs (GPs) and generalized geometric programs (GGPs).
We first convert problem (MIQQP) into an equivalent generalized bilinear programming problem with integer variables (EIQQP).
Lee and Grossmann [Lee, S., & Grossmann, I. E. (2000). New algorithms for nonlinear generalized disjunctive programming.
