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cjs94
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I'm reading about the Simplex method to solve a Linear Program. If a program is rejected as being infeasible, is there a method to identify which equations are causing it to be infeasible and a technique for reducing the program in an optimal way? (I'm not sure what 'optimal' is exactly, but my guess would be removing the fewest equations.)
My understanding is that each equation can have an artificial variable an and that the test for feasibility is that all an must be zero. If so, that leads to some questions:
Chris
My understanding is that each equation can have an artificial variable an and that the test for feasibility is that all an must be zero. If so, that leads to some questions:
- It would seem obvious then that all I have to do is identify and remove all equations where an > 0. Is that fair?
- Is is possible for one or more conflicting equations to end up with an = 0?
- Is it possible that an equation, while not causing a direct conflict, constrains two other equations such that they conflict? In that case, removing the first, single equation would be a better solution.
Chris