Sparse Non-Negative Signal Recovery from Ill-Conditioned Blurred Observations¶
- Task ID:
math.nnls_modulus_deblur - Domain:
math - Subdomain:
numerical_linear_algebra - Status:
test - Tags:
numerical-linear-algebra,nonnegative-least-squares,image-deblurring,ill-conditioned,sparse-recovery,iterative-methods
Public Summary¶
This page is generated from task metadata and selected public-safe excerpts.
Example B1 Prompt Excerpt¶
**Role:** You are a numerical-linear-algebra engineer recovering a sparse
non-negative signal from a severely ill-conditioned blurred observation.
**Task:** You are given
- `data/observation.npy` — a noisy blurred image `b` of shape `[{{image_size}}, {{image_size}}]`, dtype `float64`;
- `data/kernel.npy` — a known spatially-invariant convolution kernel `h` of shape `[{{image_size}}, {{image_size}}]`, dtype `float64`, centered at `(H//2, W//2)` and normalized so that it sums to 1;
- `data/measurement_info.json` — sidecar with `image_shape` and a rough `noise_std_estimate ≈ {{noise_std_estimate}}`.
The forward operator `A` is the **circular 2D convolution** with `h`. Its
condition number is several orders of magnitude with many singular values
Notes¶
- This page is a generated site artifact.
- Higher-level prompt details and internal benchmark specifics may remain intentionally undisclosed.