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Start an ordinary phase one of a 2-phase simplex algorithm

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linopt::Transparent::phaseI_tableau explicitly starts an (ordinary) phase one of the simplex algorithm , i.e. rows associated with infeasible basic variables are multiplied with -1 and another identity matrix with new slack variables is added to the given tableau. As soon as an optimal tableau with relative costs 0 is found the calculation can be continued with linopt::Transparent::clean_basis and the second phase of the simplex algorithm (linopt::Transparent::phaseII_tableau).


Example 1

The first simplex tableau is created and the first phase of the simplex algorithm is started:

t := linopt::Transparent([{x + y >= 2}, x, NonNegative]);
t := linopt::Transparent::phaseI_tableau(t)

We can see that a new slack variable, slk2, was added to the tableau. And if we now execute linopt::Transparent::simplex we can see that we have just finished the first phase of the simplex algorithm:

t := linopt::Transparent::simplex(t):

We continue the simplex algorithm by executing linopt::Transparent::clean_basis, linopt::Transparent::phaseII_tableau and linopt::Transparent::simplex. Observe in this special case linopt::Transparent::clean_basis is not necessary:

t := linopt::Transparent::clean_basis(t):
t := linopt::Transparent::phaseII_tableau(t):
t := linopt::Transparent::simplex(t);

delete t:



A simplex tableau of domain type linopt::Transparent

Return Values

Simplex tableau of domain type linopt::Transparent.


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Nemhauser, George L; Wolsey, Laurence A: Integer and Combinatorial Optimization. New York, Wiley, 1988.

Salkin, Harvey M; Mathur, Kamlesh: Foundations of Integer Programming. North-Holland, 1989.

Neumann, Klaus; Morlock, Martin: Operations-Research. Munich, Hanser, 1993.

Duerr, Walter; Kleibohm, Klaus: Operations Research; Lineare Modelle und ihre Anwendungen. Munich, Hanser, 1992.

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Suhl, Uwe H; Szymanski, Ralf: Supernode Processing of Mixed Integer Models. Boston, Kluwer Academic Publishers, 1994.

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