arXiv Machine Learning
Aug 13
Basin: Efficient and Extensible Numerical Optimization in Rust
arXiv:2608. 11279v1 Announce Type: new Abstract: Basin is a numerical optimization library for the Rust programming language.
Basin: Efficient and Extensible Numerical Optimization in Rust
Basin is a numerical optimization library for the Rust programming language. Numerical optimization is the task of finding the inputs that minimize a function, and it is a fundamental element across the sciences: fitting a model to data, calibrating a simulation, training a machine learning model, or choosing engineering parameters that minimize cost.
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arXiv Machine Learning
Sep 15
Generation of Custom Solvers in Rust for Convex Optimization
The paper presents cvxgenrust, an open‑source tool that generates custom Rust code for solving families of parameterized convex optimization problems defined in CVXPY. It canonicalizes problem families, extracts affine maps to Clarabel cone‑program data, and produces a specialized Rust crate that updates parameters and calls Clarabel natively at runtime. The generated solver can also be exposed to Python and registered as a custom CVXPY solver, supporting a wide range of convex problems up to semidefinite and exponential‑cone programs, and demonstrates reduced runtime compared to direct CVXPY solves and performance comparable to CVXPYgen.
arXiv Machine Learning
Jun 29
Mosaic: A Benchmark Suite for Differentiable Physics Solvers
arXiv:2606. 27895v1 Announce Type: cross Abstract: Differentiable partial differential equation (PDE) solvers underpin solver-in-the-loop ML training, gradient-based optimal control, and inverse problems, yet the practical cost of obtaining correct, usable gradients from a given solver on a given problem is largely undocumented.
arXiv AI
Jul 22
PEARL: Solver-in-the-Loop Interactive Optimization Modeling from Natural Language
arXiv:2607. 18256v1 Announce Type: new Abstract: Optimization modeling is the process of translating real-world decision problems, often described in natural language, into formal mathematical formulations and executable solver code.
arXiv AI
Sep 15
SAILOR: Solver-Assisted Interactive LLM-based Optimization Recovery
SAILOR is a proof‑of‑concept system that helps language models translate natural‑language optimization problem descriptions into executable code by detecting missing numerical values. It asks users targeted follow‑up questions, prioritizing them based on uncertainty and solver estimates of impact, and updates the model before returning a solution. In tests on 1,723 benchmark instances, SAILOR achieved exact objective‑value agreement between 27.0% and 87.6% while asking an average of 1.4–5.7 questions per instance.
arXiv Machine Learning
Jul 21
One-shot acceleration of transient PDE solvers via online-learned preconditioners
arXiv:2509. 08765v4 Announce Type: replace-cross Abstract: Data-driven acceleration of scientific computing workflows has been a high-profile aim of machine learning (ML) for science, with numerical simulation of transient partial differential equations (PDEs) being one of the main applications.
arXiv AI
Jul 7
A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
arXiv:2509. 08269v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly integrated with evolutionary computation to support optimization tasks.
arXiv Machine Learning
Aug 11
A Hybrid Nested Harness for Decoupling Structure and Parameters in LLM-Driven Optimization
arXiv:2608. 08156v1 Announce Type: new Abstract: In evolutionary algorithms powered by language models, the LLM acts as a single operator that simultaneously updates structural components (like control flow) and continuous parameters.
arXiv AI
Jun 3
Introduction to optimization methods for training SciML models
arXiv:2601. 10222v2 Announce Type: replace-cross Abstract: Optimization is central to both modern machine learning (ML) and scientific machine learning (SciML), yet the structure of the underlying optimization problems differs substantially across these domains.
arXiv AI
Aug 5
Large language models for partial differential equation workflows
arXiv:2608. 03600v1 Announce Type: new Abstract: Partial differential equations (PDEs) become actionable in science and engineering not as isolated formulae, but as executable workflows that connect modelling assumptions, governing equations, numerical solvers, diagnostics, and decisions.
arXiv Machine Learning
2d ago
CraftSPH: A high-accuracy and composable differentiable SPH solver implemented in PyTorch
arXiv:2609.38208v1 Announce Type: cross Abstract: Smoothed Particle Hydrodynamics (SPH) is well suited to a range of problems, particularly those involving large deformations in fluid dynamics. In re...
arXiv AI
Jul 16
Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code
arXiv:2607. 13921v1 Announce Type: cross Abstract: Languages with rich static semantics, such as Rust, provide stronger guarantees for AI-generated code, but their strictness makes generation more difficult.