# median

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median overloads median operator

### Syntax

m = median(X)


### Examples

The following silly example defines a regression problem with the constraint that the mean and the median of the decision variable are equal. As usual, we add explicit bound constraints to improve the big-M reformulations.

A = randn(20,5);
b = randn(20,1)*20;
x = sdpvar(5,1);

e = b-A*x;
F = [mean(x) == median(x), -100 <= x <= 100];
optimize(F,norm(e,1));


median builds on the sort operator which is extremely expensive. Sorting a variable of with $$n$$ elements requires $$n^2$$ binary variables. A more efficient integer model can be developed, make a feature request if your need this.