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New GPU solver accelerates symbolic regression constant optimization

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel GPU-resident solver for optimizing constants in symbolic regression using tree-based genetic programming. This batched Levenberg-Marquardt solver efficiently processes a heterogeneous population of expression trees with minimal CUDA launches per iteration. It leverages reverse-mode automatic differentiation for Jacobian assembly and a double-precision guard to ensure constant quality, achieving high throughput on NVIDIA A100 hardware and outperforming existing methods. AI

IMPACT This research could lead to more efficient discovery of governing equations in scientific modeling and AI development.

RANK_REASON The cluster contains an academic paper detailing a new method and implementation for a specific computational task.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New GPU solver accelerates symbolic regression constant optimization

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Hao Mao, Xu Tony Liu, Shuai Lu, Peng Zhao, Wenzheng Jiang, Yuntian Chen ·

    Efficient Constant Optimization for Symbolic Regression with GPU-Accelerated Tree-Based Genetic Programming

    arXiv:2609.03352v1 Announce Type: cross Abstract: Constant optimization refines the numerical coefficients of candidate expressions in tree-based genetic programming for symbolic regression. But its per-generation cost has led modern GPU-accelerated frameworks to omit it or restr…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Yuntian Chen ·

    Efficient Constant Optimization for Symbolic Regression with GPU-Accelerated Tree-Based Genetic Programming

    Constant optimization refines the numerical coefficients of candidate expressions in tree-based genetic programming for symbolic regression. But its per-generation cost has led modern GPU-accelerated frameworks to omit it or restrict it to lightweight forms. We present a GPU-resi…

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