Algorithm 1.

Algorithm for optimizing sparse regression vector

Input: A, b, s, α.	▷ Input data A, predicted data b, sparseness level s, step size α.
x0 ← random seed.	▷ Initialize regression vector.
p0 ← AT (b − Ax) − λ1x − λ2Δx	▷ Initialize direction with negative of gradient.
r0 ← p0	▷ Initialize residual.
k ← 0	▷ Initialize iterator.
while not converged do
xk+1 ← xk + αpk	▷ Update solution.
γopt ← G(xk+1, s)	▷ Find appropriate value of γ for desired sparsity.
xk+1 ← S(xk+1, γopt)	▷ Project solution to entry in sparse feasible set.
rk+1 ← AT (b − Axk+1) − λ1xk+1 + λ2Δxk+1	▷ Update residual.
$\beta \leftarrow \frac{r_k^{\mathrm{T}}{r}_k}{r_{k-1}^{\mathrm{T}}{r}_{k-1}}$
pk ← rk + βpk−1	▷ Update direction.
k ← k +1.
end while
Output: xk.