In a linear optimization problem, objective function, coefficients matrix, and the right-hand side might be perturbed with distinct parameters independently. For such a problem, we are interested in finding the region that contains the origin, and the optimal partition remains invariant. A computational methodology is presented here for detecting the boundary of this region. The cases where perturbation occurs only in the coefficients matrix and right-hand side vector or the objective function are specified as special cases. The findings are illustrated with some simple examples.
Mehanfar, N., & Ghaffari Hadigheh, A. (2022). Optimal partition invariancy in multi-parametric linear optimization. Journal of Mathematical Modeling, 10(3), 433-448. doi: 10.22124/jmm.2022.20758.1809
MLA
Nayyer Mehanfar; Alireza Ghaffari Hadigheh. "Optimal partition invariancy in multi-parametric linear optimization". Journal of Mathematical Modeling, 10, 3, 2022, 433-448. doi: 10.22124/jmm.2022.20758.1809
HARVARD
Mehanfar, N., Ghaffari Hadigheh, A. (2022). 'Optimal partition invariancy in multi-parametric linear optimization', Journal of Mathematical Modeling, 10(3), pp. 433-448. doi: 10.22124/jmm.2022.20758.1809
VANCOUVER
Mehanfar, N., Ghaffari Hadigheh, A. Optimal partition invariancy in multi-parametric linear optimization. Journal of Mathematical Modeling, 2022; 10(3): 433-448. doi: 10.22124/jmm.2022.20758.1809
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