A sharp signal is observed only after a Gaussian blur and a little added noise. We reconstruct it by choosing its twelve samples to fit the observation, regularised by a smoothness penalty on jumps between adjacent samples. This is the classic regularised least-squares inverse problem: smooth and near-convex, with one well-defined optimum. Score is reconstruction residual plus smoothness penalty, to minimise. 12-D.
| Algorithm | Score | Evals |
|---|---|---|
| — no runs yet — | ||
The JS objective is a line-for-line port of
example_applications/signal_deconvolution and agrees to floating-point
tolerance. The dashed line is the hidden true signal, the cyan line is the blurred
observation, and the bars are the optimizer's reconstruction.