A central theme in combinatorial optimization is to represent a set of feasible solutions as a set of integer points X in an appropriate space and to optimize a linear function over them. To treat this problem algorithmically, a common approach is to devise a system of linear inequalities whose feasible integer points are precisely X. The smallest number of inequalities in such a system that does not use auxiliary variables is called the relaxation complexity rc(X). In this talk, we discuss the relaxation complexity of the arguably simplest full-dimensional set of integer points, the discrete standard simplex. Surprisingly, the number of inequalities needed in a relaxation depends strongly on the coefficients that are allowed: While it is known that a linear number of inequalities is needed when only using rational coefficients, we present an explicit and elementary construction showing that a logarithmic number of inequalities suffices when irrational coefficients are allowed.