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. and 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
Mehanfar, N. , and Ghaffari Hadigheh, A. . "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
CHICAGO
N. Mehanfar and A. 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
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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