Active set

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In optimization, a problem is defined using an objective function to minimize or maximize, and a set of constraints

$g_1(x)\ge 0, \dots, g_k(x)\ge 0$

that define the feasible region, that is, the set of all x to search for the optimal solution. Given a point $x$ in the feasible region, a constraint

$g_i(x) \ge 0$

is called active at $x$ if $g i (x) = 0$ and inactive at $x$ if $g i (x) > 0.$ The active set at $x$ is made up of those constraints $g i (x)$ that are active at the current point.

The active set is particularly important in optimization theory as it determines which constraints will influence the final result of optimization. For example, in solving the linear programming problem, the active set gives the hyperplanes that intersect at the solution point. In quadratic programming, as the solution is not mandatorily on one of the edges of the bounding polygon, an estimation of the active set gives us a subset of inequalities to watch while searching the solution, which reduces the complexity of the search.

In general an active set algorithm has the following structure:

Find a feasible starting point

repeat until "optimal enough"

solve the equality problem defined by the active set

compute the lagrangians of the active set

remove a subset of the constraints with negative lagrangians

search for infeasible constraints

end repeat