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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. 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,

  4. Simplex algorithm - Wikipedia

    en.wikipedia.org/wiki/Simplex_algorithm

    Second, for each remaining inequality constraint, a new variable, called a slack variable, is introduced to change the constraint to an equality constraint. This variable represents the difference between the two sides of the inequality and is assumed to be non-negative. For example, the inequalities

  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. Mathematical optimization - Wikipedia

    en.wikipedia.org/wiki/Mathematical_optimization

    Many optimization algorithms need to start from a feasible point. One way to obtain such a point is to relax the feasibility conditions using a slack variable; with enough slack, any starting point is feasible. Then, minimize that slack variable until the slack is null or negative.

  7. Big M method - Wikipedia

    en.wikipedia.org/wiki/Big_M_method

    For less-than or equal constraints, introduce slack variables s i so that all constraints are equalities. Solve the problem using the usual simplex method. For example, x + y ≤ 100 becomes x + y + s 1 = 100, whilst x + y ≥ 100 becomes x + y − s 1 + a 1 = 100. The artificial variables must be shown to be 0.

  8. Slack bus - Wikipedia

    en.wikipedia.org/wiki/Slack_bus

    The voltage magnitude is also assumed to be 1 p.u. at the slack bus. The slack bus provides or absorbs active and reactive power to and from the transmission line to provide for losses, since these variables are unknown until the final solution is established. The slack bus is the only bus for which the system reference phase angle is defined ...

  9. 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 ...