Summary: Feature selection often constitutes one of the central aspects of many scientific investigations. Among the methodologies for feature selection in penalized regression, the smoothly clipped and absolute deviation seems to be very useful because it satisfies the oracle property. However, its estimation algorithms such as the local quadratic approximation and the concave-convex procedure are not computationally efficient. In this paper, we propose an efficient penalization path algorithm. Through numerical examples on real and simulated data, we illustrate that our path algorithm can be useful for feature selection in regression problems.