 Set Inversion
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Set Inversion

In mathematics, set inversion is the problem of characterizing the preimage X of a set Y by a function f, i.e., X = f-1(Y) = {x ? Rn | f(x) ? Y}. It can also be viewed as the problem of describing the solution set of the quantified constraint "Y(f(x))", where Y(y) is a constraint, for example, an inequality, describing the set Y.

In most applications, f is a function from Rn to Rp and the set Y is a box of Rp (i.e. a Cartesian product of p intervals of R).

When f is nonlinear the set inversion problem can be solved  using interval analysis combined with a branch-and-bound algorithm. 

The main idea consists in building a paving of Rp made with non-overlapping boxes. For each box [x], we perform the following tests:

1. if f([x]) ? Y we conclude that [x] ? X;
2. if f([x]) ? Y = ? we conclude that [x] ? X = ?;
3. Otherwise, the box [x] the box is bisected except if its width is smaller than a given precision.

To check the two first tests, we need an interval extension (or an inclusion function) [f] for f. Classified boxes are stored into subpavings, i.e., union of non overlapping boxes. The algorithm can be made more efficient by replacing the inclusion tests by contractors.

## Example

The set X = f-1([4,9]) where f(x1, x2) = x2
1
+ x2
2
is represented on the figure.

For instance, since [-2,1]2 + [4,5]2 = [0,4] + [16,25] = [16,29] does not intersect the interval [4,9], we conclude that the box [-2,1] × [4,5] is outside X. Since [-1,1]2 + [2,]2 = [0,1] + [4,5] = [4,6] is inside [4,9], we conclude that the whole box [-1,1] × [2,] is inside X.

## Application

Set inversion is mainly used for path planning, for nonlinear parameter set estimation  , for localization  or for the characterization of stability domains of linear dynamical systems.  .