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  2. Slack variable - Wikipedia

    en.wikipedia.org/wiki/Slack_variable

    Slack variables give an embedding of a polytope into the standard f-orthant, where is the number of constraints (facets of the polytope). This map is one-to-one (slack variables are uniquely determined) but not onto (not all combinations can be realized), and is expressed in terms of the constraints (linear functionals, covectors).

  3. Big M method - Wikipedia

    en.wikipedia.org/wiki/Big_M_method

    For any greater-than constraints, introduce surplus s i and artificial variables a i (as shown below). Choose a large positive Value M and introduce a term in the objective of the form −M multiplying the artificial variables. For less-than or equal constraints, introduce slack variables s i so that all constraints are equalities.

  4. Basic feasible solution - Wikipedia

    en.wikipedia.org/wiki/Basic_feasible_solution

    A BFS can have less than m non-zero variables; in that case, it can have many different bases, all of which contain the indices of its non-zero variables. 3. A feasible solution x {\displaystyle \mathbf {x} } is basic if-and-only-if the columns of the matrix A K {\displaystyle A_{K}} are linearly independent, where K is the set of indices of ...

  5. Linear programming - Wikipedia

    en.wikipedia.org/wiki/Linear_programming

    So if the i-th slack variable of the primal is not zero, then the i-th variable of the dual is equal to zero. Likewise, if the j-th slack variable of the dual is not zero, then the j-th variable of the primal is equal to zero. This necessary condition for optimality conveys a fairly simple economic principle.

  6. Simplex algorithm - Wikipedia

    en.wikipedia.org/wiki/Simplex_algorithm

    The variables corresponding to the columns of the identity matrix are called basic variables while the remaining variables are called nonbasic or free variables. If the values of the nonbasic variables are set to 0, then the values of the basic variables are easily obtained as entries in b {\displaystyle \mathbf {b} } and this solution is a ...

  7. Linear complementarity problem - Wikipedia

    en.wikipedia.org/wiki/Linear_complementarity_problem

    with v the Lagrange multipliers on the non-negativity constraints, λ the multipliers on the inequality constraints, and s the slack variables for the inequality constraints. The fourth condition derives from the complementarity of each group of variables (x, s) with its set of KKT vectors (optimal Lagrange multipliers) being (v, λ). In that case,

  8. Resource slack - Wikipedia

    en.wikipedia.org/wiki/Resource_slack

    Resource slack, in the business and management literature, is the level of availability of a resource. Resource slack can be considered as the opposite of resource scarcity or resource constraints. The availability of resources can therefore be defined in terms of resource slack versus constraints, as two ends of a continuum. [1]

  9. Constrained optimization - Wikipedia

    en.wikipedia.org/wiki/Constrained_optimization

    Every variable is associated a bucket of constraints; the bucket of a variable contains all constraints having the variable has the highest in the order. Bucket elimination proceed from the last variable to the first. For each variable, all constraints of the bucket are replaced as above to remove the variable.