  In this paper, we consider the problem of obtaining the best $k$-sparse
solution of $Ax=y$ subject to the constraint that the columns of $A$ are
orthogonal. The naive approach for obtaining a solution to this problem has
exponential complexity and there exist $l_1$ regularization methods such as
Lasso to obtain approximate solutions. In this paper, we show that we can
obtain an exact solution to the problem, with much less computational effort
compared to the brute force search when the columns of $A$ are orthogonal.
