1. Thus I have a parametrization of the simplex using slack variables . 2. Then, minimize that slack variable until slack is null or negative. 3. Introducing a slack variable replaces an inequality constraint with an equality constraint and a nonnegativity constraint. 4. As such the M + 1 th and N + 1 th elements are slack variables . 5. Here the slack variables \ xi _ { ijl } absorb the amount of violations of the impostor constraints. 6. Introducing a new slack variable x k for this inequality, a new constraint is added to the linear program, namely 7. :In terms of computation though, it's usually better not to eliminate any of the the slack variables . 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.9. This can be formulated by an SDP . We handle the inequality constraints by augmenting the variable matrix and introducing slack variables , for example 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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