5 papers
An Agent-Based Framework for the Automatic Validation of Mathematical Optimization Models
Alexander Zadorojniy, Segev Wasserkrug, Eitan Farchi
Recently, using Large Language Models (LLMs) to generate optimization models from natural language descriptions has became increasingly popular. However, a major open question is h…
Heuristics for Combinatorial Optimization via Value-based Reinforcement Learning: A Unified Framework and Analysis
Orit Davidovich, Shimrit Shtern, Segev Wasserkrug +1
Since the 1990s, considerable empirical work has been carried out to train statistical models, such as neural networks (NNs), as learned heuristics for combinatorial optimization (…
Finding Probably Approximate Optimal Solutions by Training to Estimate the Optimal Values of Subproblems
Nimrod Megiddo, Segev Wasserkrug, Orit Davidovich +1
The paper is about developing a solver for maximizing a real-valued function of binary variables. The solver relies on an algorithm that estimates the optimal objective-function va…
Making a Case for Research Collaboration Between Artificial Intelligence and Operations Research Experts
Radhika Kulkarni, Gianluca Brero, Yu Ding +7
In 2021, INFORMS, ACM SIGAI, and the Computing Community Consortium (CCC) hosted three workshops to explore synergies between Artificial Intelligence (AI) and Operations Research (…
Achieving PAC Guarantees in Mechanism Design through Multi-Armed Bandits
Takayuki Osogami, Hirota Kinoshita, Segev Wasserkrug
We analytically derive a class of optimal solutions to a linear program (LP) for automated mechanism design that satisfies efficiency, incentive compatibility, strong budget balanc…