Neural Architecture Search Enables Automated Discovery of Sparse Recovery Algorithms

Global: Neural Architecture Search Enables Automated Discovery of Sparse Recovery Algorithms

A research team from the University of California, Berkeley, and collaborators announced a meta‑learning framework that can automatically rediscover classic sparse‑recovery methods such as ISTA and its accelerated variant FISTA. The work, submitted to arXiv on 25 December 2025, leverages neural architecture search (NAS) to explore a design space of more than 50,000 variables.

Motivation and Scope

The authors note that designing algorithms for inverse problems in signal processing typically relies on expert intuition and extensive trial‑and‑error. By framing algorithm design as a search problem, the proposed system aims to reduce development time and broaden applicability across diverse data distributions.

Methodology

Using a NAS pipeline, the researchers defined a modular search space that encodes key components of iterative shrinkage‑thresholding methods. The framework evaluates candidate architectures on synthetic reconstruction tasks, selecting those that minimize a predefined loss. Experiments demonstrate that the system successfully reproduces essential elements of ISTA and FISTA without prior knowledge of their mathematical formulation.

Results and Findings

Across multiple test scenarios, the discovered architectures matched the convergence rates of the original algorithms. The study also reports that the framework can be extended to other sparse‑recovery techniques, suggesting a pathway toward automated algorithm synthesis for broader classes of inverse problems.

Implications for the Field

If scalable, the approach could accelerate research in signal processing, computational imaging, and related domains where sparse representations are critical. By automating the discovery phase, practitioners may focus more on application‑specific constraints rather than foundational algorithmic design.

Future Directions

The authors plan to expand the search space to include non‑convex penalties and to test the system on real‑world datasets such as medical imaging and remote sensing. They also intend to open‑source the codebase to encourage community contributions.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.