MetaSG-SAEA is a bi-level meta-BBO framework that uses a meta-policy for search guidance via the MM-CCI constraint abstraction and diffusion-based population initialization to outperform baselines on expensive constrained multi-objective optimization problems.
arXiv preprint arXiv:2403.01131 , year=
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ORPilot is the first agentic LLM system built specifically for production optimization modeling, using interview, data collection, parameter computation agents and a solver-agnostic intermediate representation to handle real-world ambiguous problems and large raw datasets.
PerfCoder is a family of LLMs trained on optimization trajectories with human annotations and runtime-based preference alignment that achieves higher runtime speedups and optimization rates on the PIE benchmark than prior models while producing interpretable feedback.
ERFSL generates and optimizes LLM-based reward functions for custom multi-objective RL, correcting codes in one iteration and converging weights in 5.2 iterations on average even from 500x errors.
APF automates solver-independent formulation of optimization problems from natural language via LLMs fine-tuned on synthetically generated high-quality data, outperforming prior methods on antenna radiation efficiency tasks.
ERFSL uses LLMs to create per-requirement reward components, correct their code via a critic, and optimize weights with genetic-algorithm-style mutation and crossover driven by training logs, succeeding in a zero-shot data collection task.
citing papers explorer
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Meta-Black-Box Optimization Can Do Search Guidance for Expensive Constrained Multi-Objective Optimization
MetaSG-SAEA is a bi-level meta-BBO framework that uses a meta-policy for search guidance via the MM-CCI constraint abstraction and diffusion-based population initialization to outperform baselines on expensive constrained multi-objective optimization problems.
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ORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling
ORPilot is the first agentic LLM system built specifically for production optimization modeling, using interview, data collection, parameter computation agents and a solver-agnostic intermediate representation to handle real-world ambiguous problems and large raw datasets.
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PerfCoder: Large Language Models for Interpretable Code Performance Optimization
PerfCoder is a family of LLMs trained on optimization trajectories with human annotations and runtime-based preference alignment that achieves higher runtime speedups and optimization rates on the PIE benchmark than prior models while producing interpretable feedback.
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ERFSL: An Efficient Reward Function Searcher via Language Models for Custom-Environment Multi-Objective Optimization (Student Abstract)
ERFSL generates and optimizes LLM-based reward functions for custom multi-objective RL, correcting codes in one iteration and converging weights in 5.2 iterations on average even from 500x errors.
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Solver-Independent Automated Problem Formulation via LLMs for High-Cost Simulation-Driven Design
APF automates solver-independent formulation of optimization problems from natural language via LLMs fine-tuned on synthetically generated high-quality data, outperforming prior methods on antenna radiation efficiency tasks.
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Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement
ERFSL uses LLMs to create per-requirement reward components, correct their code via a critic, and optimize weights with genetic-algorithm-style mutation and crossover driven by training logs, succeeding in a zero-shot data collection task.