AutoOpt: A Dataset and a Unified Framework for Automating Optimization Problem Solving

• URL: http://arxiv.org/abs/2510.21436v1
• Date: Fri, 24 Oct 2025 13:14:53 GMT
• Title: AutoOpt: A Dataset and a Unified Framework for Automating Optimization Problem Solving
• Authors: Ankur Sinha, Shobhit Arora, Dhaval Pujara,
• Abstract summary: AutoOpt-11k dataset is a unique image dataset of over 11,000 single-objective, multi-objective, and handwritten mathematical optimization problems.<n>The dataset is created by 25 experts to avoid errors in data creation.<n>We develop AutoOpt, a machine learning based automated approach for optimization problems.
• Score: 0.17205106391379024
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study presents AutoOpt-11k, a unique image dataset of over 11,000 handwritten and printed mathematical optimization models corresponding to single-objective, multi-objective, multi-level, and stochastic optimization problems exhibiting various types of complexities such as non-linearity, non-convexity, non-differentiability, discontinuity, and high-dimensionality. The labels consist of the LaTeX representation for all the images and modeling language representation for a subset of images. The dataset is created by 25 experts following ethical data creation guidelines and verified in two-phases to avoid errors. Further, we develop AutoOpt framework, a machine learning based automated approach for solving optimization problems, where the user just needs to provide an image of the formulation and AutoOpt solves it efficiently without any further human intervention. AutoOpt framework consists of three Modules: (i) M1 (Image_to_Text)- a deep learning model performs the Mathematical Expression Recognition (MER) task to generate the LaTeX code corresponding to the optimization formulation in image; (ii) M2 (Text_to_Text)- a small-scale fine-tuned LLM generates the PYOMO script (optimization modeling language) from LaTeX code; (iii) M3 (Optimization)- a Bilevel Optimization based Decomposition (BOBD) method solves the optimization formulation described in the PYOMO script. We use AutoOpt-11k dataset for training and testing of deep learning models employed in AutoOpt. The deep learning model for MER task (M1) outperforms ChatGPT, Gemini and Nougat on BLEU score metric. BOBD method (M3), which is a hybrid approach, yields better results on complex test problems compared to common approaches, like interior-point algorithm and genetic algorithm.

Related papers

• URDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model [76.08429266631823]
  We propose an end-to-end automatic reconstruction framework based on a 3D multimodal large language model (MLLM)<n>URDF-Anything utilizes an autoregressive prediction framework based on point-cloud and text multimodal input to jointly optimize geometric segmentation and kinematic parameter prediction.<n> Experiments on both simulated and real-world datasets demonstrate that our method significantly outperforms existing approaches.
  arXiv  Detail & Related papers  (2025-11-02T13:45:51Z)
• Table2LaTeX-RL: High-Fidelity LaTeX Code Generation from Table Images via Reinforced Multimodal Language Models [53.03670032402846]
  We address the task of table image to code generation, with the goal of automating the reconstruction of high-quality, publication-ready tables from visual inputs.<n>A central challenge of this task lies in accurately handling complex tables -- those with large sizes, deeply nested structures, and semantically rich or irregular cell content.<n>We propose a reinforced multimodal large language model (MLLM) framework, where a pre-trained MLLM is fine-tuned on a large-scale table-to-La dataset.
  arXiv  Detail & Related papers  (2025-09-22T11:13:48Z)
• Toward a Trustworthy Optimization Modeling Agent via Verifiable Synthetic Data Generation [11.988926173584154]
  We present a framework for training trustworthy large language model (LLM) agents via a synthetic data generation pipeline.<n> OptiTrust is a modular LLM agent that performs multi-language translation from natural language to solver-ready code.<n>Our agent achieves state-of-the-art performance on standard benchmarks.
  arXiv  Detail & Related papers  (2025-08-05T05:54:20Z)
• OptimAI: Optimization from Natural Language Using LLM-Powered AI Agents [8.441638148384389]
  We introduce OptimAI, a framework for solving Optimization problems described in natural language.<n>Our framework is built upon the following key roles: formulator, planner, coder and code critic.<n>Our approach attains 88.1% accuracy on the NLP4LP dataset and 82.3% on the Optibench dataset, reducing error rates by 58% and 52%, respectively, over prior best results.
  arXiv  Detail & Related papers  (2025-04-23T17:45:05Z)
• OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling [9.617742955894247]
  Lack of high-quality optimization modeling datasets hampers large language models.<n>We propose a scalable framework for synthesizing a high-quality dataset, named OptMATH.<n>We demonstrate that models of various sizes trained on OptMATH achieve superior results on multiple modeling benchmarks.
  arXiv  Detail & Related papers  (2025-02-16T12:38:37Z)
• Autoformulation of Mathematical Optimization Models Using LLMs [50.030647274271516]
  This paper approaches the problem of $textitautoformulation$: the automated creation of solver-ready optimization models from natural language problem descriptions.<n>We identify three core challenges of autoformulation: $textit(1)$ the vast, problem-dependent hypothesis space, and $textit(2)$ efficient and diverse exploration of this space under uncertainty.<n>We present a novel method leveraging $textitLarge Language Models$ with $textitMonte-Carlo Tree Search$, exploiting the hierarchical nature of optimization modeling to generate and systematically explore possible formulations
  arXiv  Detail & Related papers  (2024-11-03T20:41:38Z)
• OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling [62.19438812624467]
  Large language models (LLMs) have exhibited their problem-solving abilities in mathematical reasoning.<n>We propose OptiBench, a benchmark for End-to-end optimization problem-solving with human-readable inputs and outputs.
  arXiv  Detail & Related papers  (2024-07-13T13:27:57Z)
• MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine [85.80851893886161]
  We propose MAVIS, a MAthematical VISual instruction tuning pipeline for MLLMs, featuring an automatic data engine to efficiently create mathematical visual datasets. We use MAVIS-Caption to fine-tune a math-specific vision encoder (CLIP-Math) through contrastive learning, tailored for improved diagram visual encoding. Third, we adopt MAVIS-Instruct to perform the instruction tuning for robust problem-solving skills, and term the resulting model as MAVIS-7B.
  arXiv  Detail & Related papers  (2024-07-11T17:59:47Z)
• Solving General Natural-Language-Description Optimization Problems with Large Language Models [34.50671063271608]
  We propose a novel framework called OptLLM that augments LLMs with external solvers. OptLLM accepts user queries in natural language, convert them into mathematical formulations and programming codes, and calls the solvers to calculate the results. Some features of OptLLM framework have been available for trial since June 2023.
  arXiv  Detail & Related papers  (2024-07-09T07:11:10Z)
• Decoding-Time Language Model Alignment with Multiple Objectives [116.42095026960598]
  Existing methods primarily focus on optimizing LMs for a single reward function, limiting their adaptability to varied objectives. Here, we propose $textbfmulti-objective decoding (MOD)$, a decoding-time algorithm that outputs the next token from a linear combination of predictions. We show why existing approaches can be sub-optimal even in natural settings and obtain optimality guarantees for our method.
  arXiv  Detail & Related papers  (2024-06-27T02:46:30Z)
• AutoML-GPT: Large Language Model for AutoML [5.9145212342776805]
  We have established a framework called AutoML-GPT that integrates a comprehensive set of tools and libraries. Through a conversational interface, users can specify their requirements, constraints, and evaluation metrics. We have demonstrated that AutoML-GPT significantly reduces the time and effort required for machine learning tasks.
  arXiv  Detail & Related papers  (2023-09-03T09:39:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.