Before revision:  Often the regression may be high dimensional, depending on many variables. This paper gives minimal conditions for root-n consistent and efficient estimation of such objects when the regression and the Riesz representer of the functional are approximately sparse and the sum of the absolute value of the coefficients is bounded. The approximately sparse functions we consider are those where an approximation by some t regressors has root mean square error less than or equal to Ct^{-\xi for C, \xi>0.
After revision:  Often the regression may be high dimensional, for C, \xi>0.