Relaxation Methods for Pure And Mixed Integer Programming Problems

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Relaxation Methods for Pure And Mixed Integer Programming Problems
George Anthony Gorry
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3) by generalized LP (this is the suggestion of Brooks and Geoffrion [2]) rather than generate all the faces of [V] . The details for doing this will be omitted because they are given for a very similar problem in reference 10. We simply mention that a gropp optimization problem defined over 0\ is used to generate solutions for a master LP problem involving the constraints (4. 3b) and (4. 3c). This problem is of the form min (c + 6°(N - Q) + tt Q)v (4. 4) s. T. V e V 14 where and tt are the (ge...neralized) LP multipliers from the master. The vector 9 is unconstrained in sign, and the vector it >_ 0. Clearly, 9 "prices out" the inaccuracies due to rounding, while tt "prices out" the resources d available for finding an optimal correction v in the rounded problem.
Let v denote an optimal solution in (4. 4). The cut (c + 9°(N - Q) +ti°Q)v > (c + 9°(N - Q) + tt°Q)v° (4. 5) is a valid Gomory cut and can be written with respect to any multipliers 6 and tt _> 0. However, a particularly strong cut is obtained if 9 and tt are the optimal shadow prices in the LP problem (4.


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