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A streamlined artificial variable free version of simplex method.

Inayatullah S, Touheed N, Imtiaz M - PLoS ONE (2015)

Bottom Line: This paper proposes a streamlined form of simplex method which provides some great benefits over traditional simplex method.For instance, it does not need any kind of artificial variables or artificial constraints; it could start with any feasible or infeasible basis of an LP.Last but not the least, it provides a teaching aid for the teachers who want to teach feasibility achievement as a separate topic before teaching optimality achievement.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematical Sciences, University of Karachi, Karachi, Pakistan.

ABSTRACT
This paper proposes a streamlined form of simplex method which provides some great benefits over traditional simplex method. For instance, it does not need any kind of artificial variables or artificial constraints; it could start with any feasible or infeasible basis of an LP. This method follows the same pivoting sequence as of simplex phase 1 without showing any explicit description of artificial variables which also makes it space efficient. Later in this paper, a dual version of the new method has also been presented which provides a way to easily implement the phase 1 of traditional dual simplex method. For a problem having an initial basis which is both primal and dual infeasible, our methods provide full freedom to the user, that whether to start with primal artificial free version or dual artificial free version without making any reformulation to the LP structure. Last but not the least, it provides a teaching aid for the teachers who want to teach feasibility achievement as a separate topic before teaching optimality achievement.

No MeSH data available.


Iteration by iteration comparison between ASM and Simplex method for example 2.
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pone.0116156.g005: Iteration by iteration comparison between ASM and Simplex method for example 2.

Mentions: The above table shows that the algorithm terminated unsuccessfully. Hence the problem is primal inconsistent. See Fig. 5 for comparison with simplex method.


A streamlined artificial variable free version of simplex method.

Inayatullah S, Touheed N, Imtiaz M - PLoS ONE (2015)

Iteration by iteration comparison between ASM and Simplex method for example 2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4358952&req=5

pone.0116156.g005: Iteration by iteration comparison between ASM and Simplex method for example 2.
Mentions: The above table shows that the algorithm terminated unsuccessfully. Hence the problem is primal inconsistent. See Fig. 5 for comparison with simplex method.

Bottom Line: This paper proposes a streamlined form of simplex method which provides some great benefits over traditional simplex method.For instance, it does not need any kind of artificial variables or artificial constraints; it could start with any feasible or infeasible basis of an LP.Last but not the least, it provides a teaching aid for the teachers who want to teach feasibility achievement as a separate topic before teaching optimality achievement.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematical Sciences, University of Karachi, Karachi, Pakistan.

ABSTRACT
This paper proposes a streamlined form of simplex method which provides some great benefits over traditional simplex method. For instance, it does not need any kind of artificial variables or artificial constraints; it could start with any feasible or infeasible basis of an LP. This method follows the same pivoting sequence as of simplex phase 1 without showing any explicit description of artificial variables which also makes it space efficient. Later in this paper, a dual version of the new method has also been presented which provides a way to easily implement the phase 1 of traditional dual simplex method. For a problem having an initial basis which is both primal and dual infeasible, our methods provide full freedom to the user, that whether to start with primal artificial free version or dual artificial free version without making any reformulation to the LP structure. Last but not the least, it provides a teaching aid for the teachers who want to teach feasibility achievement as a separate topic before teaching optimality achievement.

No MeSH data available.