TACIT: Optimization Models that Learn from Their Mistakes
math.OC, cs.AI
Submitted: 2026-09-29
Updated: 2026-09-29
Sources
- Learning Implicit Feasibility Constraints for Real-World Routing and Scheduling: Application to Log Transportation
- OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale
- OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents
- ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction and Behavioral Rationality in Operations Research
- Autoformulation of Mathematical Optimization Models Using LLMs
- Optimization with Constraint Learning: A Framework and Survey
- Inverse Mixed-Integer Programming: Learning Constraints then Objective Functions
- Learning Mixed-Integer Linear Programs from Contextual Examples
- OptimAI: Optimization from Natural Language Using LLM-Powered AI Agents
- A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions
Related papers
- Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise
- Adam-HNAG: A Convergent Reformulation of Adam with Accelerated Rate
- Incremental Learning in Mirror Flows
- Second-Order Optimization with Asynchronous Curvature
- Online Control via Counterfactual Tracking
- Asynchronous Replanning in Two Population Linear Quadratic Mean Field Games: Information Requirements and Stability