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The article discusses the pge-jax implementation of the Prioritized Grammar Enumeration (PGE) algorithm for symbolic regression. This system automatically discovers mathematical formulas from data using a fully JAX-native evaluation pipeline. Key features include GPU/TPU acceleration, automatic differentiation, and no external machine learning dependencies.
- ▪pge-jax is a complete symbolic regression system that discovers mathematical formulas from data.
- ▪The algorithm uses a grammar-based approach to generate candidate expressions and employs multi-objective optimization for selection.
- ▪Key advantages include GPU/TPU acceleration, JIT compilation, and efficient gradient-based optimization.
Opening excerpt (first ~120 words) tap to expand
pge-jax JAX implementation of the Prioritized Grammar Enumeration (PGE) algorithm for symbolic regression. Overview pge-jax is a complete symbolic regression system that automatically discovers mathematical formulas from data. It enumerates candidate expressions from a grammar, fits their coefficients using JAX-native Levenberg-Marquardt optimization, and selects the best models using a multi-objective pareto front (NSGA-II). The key advantage over prior implementations (pypge, go-pge) is a fully JAX-native evaluation pipeline, enabling: GPU/TPU acceleration of model evaluation and Jacobian computation JIT compilation via jax.jit and jax.vmap Automatic differentiation for efficient gradient-based optimization No external ML dependencies — no scikit-learn, lmfit, or DEAP required Quick…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.