An interior boundary pivotal solution algorithm for linear programmes with the optimal solution-based sensitivity region

Hossein Arsham and Angappa Gunasekaran

International Journal of Mathematics in Operational Research, 2013, vol. 5, issue 6, 663-692

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.

Keywords: linear programming; full gradient simplex algorithm; artificial-free; pivoting algorithm; feasible direction method; simplex standard-form free; big-M free; largest sensitivity region. (search for similar items in EconPapers)
Date: 2013
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