arXiv AI
LLM-Guided Evolution of Symbolic Acceleration Inequality Families for MILP
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Teaching LLMs to Generate Challenging MILP Instances via Solver Feedback
arXiv:2609.37356v1 Announce Type: new Abstract: Generating optimization instances that are both feasible and computationally challenging is crucial for benchmarking solvers and training learning-base...
arXiv AI
Aug 21
Learning Early-to-Final Solution Consistency for MILP Acceleration
arXiv:2608. 19953v1 Announce Type: new Abstract: Mixed-Integer Linear Programming (MILP) is a fundamental problem class in operations research and combinatorial optimization, with broad applications to industrial decision-making.
Hugging Face Trending Papers
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Learning Early-to-Final Solution Consistency for MILP Acceleration
Mixed-Integer Linear Programming (MILP) is a fundamental problem class in operations research and combinatorial optimization, with broad applications to industrial decision-making. Owing to their NP-hardness, however, modern solvers may struggle to find high-quality solutions for challenging MILP instances within practical time limits.
arXiv Machine Learning
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Efficient Constant Optimization for Symbolic Regression with GPU-Accelerated Tree-Based Genetic Programming
The paper introduces a GPU-resident, batched Levenberg–Marquardt solver that efficiently optimizes constants in tree-based genetic programming for symbolic regression. By using reverse-mode automatic differentiation to assemble per-tree Jacobians in a single backward sweep, the solver’s per-iteration cost becomes independent of the number of constants per tree, achieving up to 510,000 trees per second on an NVIDIA A100. Integrated into EvoGP, the solver enables end-to-end search that recovers governing equations on 10 of 18 constructed problems, a significant improvement over stock EvoGP.
arXiv Machine Learning
Oct 8
Accelerating Floating-Point Satisfiability Solving via Gradient Normalization
GradSAT is a new framework that improves satisfiability solving for Quantifier-Free Floating-Point formulas by treating each SMT clause as an independent multi‑task learning problem. It uses dynamic gradient normalization (GradNorm) to balance gradient magnitudes across clauses, preventing a few difficult clauses from dominating the optimization trajectory. The approach combines a GPU‑accelerated PyTorch backend for continuous relaxation with a precise local search engine, yielding a robust, highly parallelizable solver.
arXiv AI
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Internalizing Geometric Law: Learning from Solver Residuals for Precision-Critical Generation
arXiv:2606. 09278v1 Announce Type: cross Abstract: Large Language Models frequently hallucinate in precision-critical domains such as technical diagramming and mechanical design, where outputs must satisfy strict geometric constraints.