arXiv:2602.09530v2 Announce Type: replace-cross Abstract: We introduce AutoSpec, a neural network framework for discovering iterative spectral algorithms for large-scale numerical linear algebra and numerical optimization. Our self-supervised models adapt to input operators using coarse spectral information (e.g., eigenvalue estimates and residual norms), and predict recurrence coefficients for computing or applying a matrix polynomial tailored to a downstream task. The effectiveness of AutoSpec relies on three ingredients: an architecture whose inference pass implements short, executable nume
The increasing scale and complexity of numerical linear algebra problems in AI and scientific computing necessitate more efficient, specialized algorithms that can be discovered rather than hand-engineered.
This represents a significant advancement in algorithmic discovery, potentially accelerating research and application development in fields heavily reliant on numerical computation, from AI to engineering simulations.
The ability of neural networks to 'learn to discover' iterative spectral algorithms automates a critical and often bottlenecked aspect of high-performance computing, shifting from human intuition to AI-driven optimization.
- · AI/ML Research Firms
- · High-Performance Computing (HPC) Sector
- · Scientific Computing Software Developers
- · Large-scale Data Analysis Platforms
- · Traditional Numerical Algorithm Developers (if they don't adapt)
- · Companies reliant on inefficient legacy numerical methods
Faster and more efficient computations for large-scale AI models and scientific simulations become possible.
Reduced computational costs and accelerated discovery cycles across various scientific and engineering disciplines.
New classes of problems become tractable due to the availability of highly optimized, automatically discovered algorithms, fundamentally expanding the scope of AI applications.
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Read at arXiv cs.AI