| 1. | Thus I have a parametrization of the simplex using slack variables.
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| 2. | Then, minimize that slack variable until slack is null or negative.
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| 3. | Introducing a slack variable replaces an inequality constraint with an equality constraint and a nonnegativity constraint.
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| 4. | As such the M + 1 th and N + 1 th elements are slack variables.
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| 5. | Here the slack variables \ xi _ { ijl } absorb the amount of violations of the impostor constraints.
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| 6. | Introducing a new slack variable x k for this inequality, a new constraint is added to the linear program, namely
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| 7. | :In terms of computation though, it's usually better not to eliminate any of the the slack variables.
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| 8. | Slack variables are usually added into the above to allow for errors and to allow approximation in the case the above problem is infeasible.
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| 9. | This can be formulated by an SDP . We handle the inequality constraints by augmenting the variable matrix and introducing slack variables, for example
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| 10. | This map is one-to-one ( slack variables are uniquely determined ) but not onto ( not all combinations can be realized ).
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