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• W2056543148 abstract "We have developed a full gradient method that consists of three phases. The initialisation phase provides the initial tableau that may not have a full set of basis. The push phase uses a full gradient vector of the objective function to obtain a feasible vertex. This is then followed by a series of pivotal steps using the sub-gradient, which leads to an optimal solution (if exists) in the final iteration phase. At each of these iterations, the sub-gradient provides the desired direction of motion within the feasible region. The algorithm hits and/or moves on the constraint hyper-planes and their intersections to reach an optimal vertex (if exists). The algorithm works in the original decision variables and slack/surplus space, therefore, there is no need to introduce any new extra variables such as artificial variables. The simplex solution algorithm can be considered as a sub-more efficient. Given a linear programme has a known unique non-degenerate primal/dual solution; we develop the largest sensitivity region for linear programming models-based only the optimal solution rather than the final tableau. It allows for simultaneous, dependent/independent changes on the cost coefficients and the right-hand side of constraint. Numerical illustrative examples are given." @default.
• W2056543148 created "2016-06-24" @default.
• W2056543148 creator A5046110173 @default.
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• W2056543148 date "2013-01-01" @default.
• W2056543148 modified "2023-10-16" @default.
• W2056543148 title "An interior boundary pivotal solution algorithm for linear programmes with the optimal solution-based sensitivity region" @default.
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• W2056543148 doi "https://doi.org/10.1504/ijmor.2013.057489" @default.
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