  This paper addresses the deconvolution problem of estimating a
square-integrable probability density from observations contaminated with
additive measurement errors having a known density. The estimator begins with a
density estimate of the contaminated observations and minimizes a
reconstruction error penalized by an integrated squared $m$-th derivative.
Theory for deconvolution has mainly focused on kernel- or wavelet-based
techniques, but other methods including spline-based techniques and this
smoothness-penalized estimator have been found to outperform kernel methods in
simulation studies. This paper fills in some of these gaps by establishing
asymptotic guarantees for the smoothness-penalized approach. Consistency is
established in mean integrated squared error, and rates of convergence are
derived for Gaussian, Cauchy, and Laplace error densities, attaining some lower
bounds already in the literature. The assumptions are weak for most results;
the estimator can be used with a broader class of error densities than the
deconvoluting kernel. Our application example estimates the density of the mean
cytotoxicity of certain bacterial isolates under random sampling; this mean
cytotoxicity can only be measured experimentally with additive error, leading
to the deconvolution problem. We also describe a method for approximating the
solution by a cubic spline, which reduces to a quadratic program.
