Best approximations of functions of several variables by sums of functions of fewer variables

The minimum of the mean-square deviation of approximations of functions of several independent variables by sums of functions of fewer variables in a multidimensional parallelepiped is investigated. The approximating function yielding the minimum mean-square deviation is obtained and this minimum deviation is calculated.