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Analog circuit design requires substantial human expertise and involvement, which is a significant roadblock to design productivity. Bayesian Optimization (BO), a popular machine-learning-based optimization strategy, has been leveraged to automate analog design given its applicability across various circuit topologies and technologies. Traditional BO methods employ black-box Gaussian Process surrogate models and optimized labeled data queries to find optimization solutions by trading off between exploration and exploitation. However, the search for the optimal design solution in BO can be expensive from both a computational and data usage point of view, particularly for high-dimensional optimization problems. This paper presents ADO-LLM, the first work integrating large language models (LLMs) with Bayesian Optimization for analog design optimization. ADO-LLM leverages the LLM’s ability to infuse domain knowledge to rapidly generate viable design points to remedy BO's inefficiency in finding high-value design areas specifically under the limited design space coverage of the BO's probabilistic surrogate model. In the meantime, sampling of design points evaluated in the iterative BO process provides quality demonstrations for the LLM to generate high-quality design points while leveraging infused broad design knowledge. Furthermore, the diversity brought by BO's exploration enriches the contextual understanding of the LLM and allows it to more broadly search in the design space and prevent repetitive and redundant suggestions. We evaluate the proposed framework on two different types of analog circuits and demonstrate notable improvements in design efficiency and effectiveness.
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This content will become publicly available on July 14, 2026
Autoformulation of Mathematical Optimization Models Using LLMs
Mathematical optimization is fundamental to decision-making across diverse domains, from operations research to healthcare. Yet, translating real-world problems into optimization models remains a difficult task, often demanding specialized expertise. This paper approaches the problem of : the automated creation of solver-ready optimization models from natural language problem descriptions. We identify three core challenges of autoformulation: the vast, problem-dependent hypothesis space, efficient and diverse exploration of this space under uncertainty, and evaluation of formulation correctness against problem description. To address these challenges, we present a novel method leveraging (LLMs) with , exploiting the hierarchical nature of optimization modeling to generate and systematically explore possible formulations. To enhance search efficiency, we introduce symbolic pruning to eliminate trivially equivalent search paths (branches), and employ LLM-based evaluation of partial formulations to guide search. Empirical analysis on linear and mixed-integer programming benchmarks demonstrates our method's effectiveness, with significant performance gains from both LLM-based value estimation and symbolic pruning techniques.
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- Award ID(s):
- 2244574
- PAR ID:
- 10615254
- Publisher / Repository:
- Proceedings of the 42nd International Conference on Machine Learning
- Date Published:
- Journal Name:
- Proceedings of Machine Learning Research
- Volume:
- PMLR 267
- ISSN:
- 2640-3498
- Format(s):
- Medium: X
- Location:
- Vancouver, BC, Canada
- Sponsoring Org:
- National Science Foundation
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