13 citations · 34 across the 10 of their papers we have counts for
12 papers
SEDIMA: Cross-Run Hierarchical Insight Memory for Evolutionary Search Agents
Amirhossein Abaskohi, Mahdi Mostajabdaveh, Zirui Zhou
Large language model (LLM)-driven evolutionary search is a powerful paradigm for automated program and algorithm discovery, yet existing systems are largely memoryless: each run ex…
COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space
Oleksandr Yakovenko, Mahdi Mostajabdaveh, Cheikh Ahmed +4
Although Vehicle Routing Problems (VRP) are essential to many real-world systems, they remain computationally intractable at scale due to their combinatorial complexity. Traditiona…
Latent Heuristic Search: Continuous Optimization for Automated Algorithm Design
Cheikh Ahmed, Mahdi Mostajabdaveh, Zirui Zhou
The integration of Large Language Models (LLMs) into evolutionary frameworks has established a new paradigm for automated heuristic discovery. Despite their promise, these methods…
MASPRM: Multi-Agent System Process Reward Model
Milad Yazdani, Mahdi Mostajabdaveh, Zirui Zhou +1
Inference-time search over multi-agent systems (MAS) wastes compute when it cannot identify which agent's intermediate message advanced progress. We present the Multi-Agent System…
Discrete Optimization of Min-Max Violation and its Applications Across Computational Sciences
Cheikh Ahmed, Mahdi Mostajabdaveh, Samin Aref +1
We introduce the Discrete Min-Max Violation (DMMV) as a general optimization problem which seeks an assignment of discrete values to variables that minimizes the largest constraint…
LLM-Guided Evolution of Symbolic Acceleration Inequality Families for MILP
Milad Yazdani, Mahdi Mostajabdaveh, Samin Aref +1
Integer programming (IP) is central to many combinatorial optimization tasks but remains challenging due to its NP-hard nature. A practical way to improve IP solvers is to manually…
SMARTAPS: Tool-augmented LLMs for Operations Management
Timothy Tin Long Yu, Mahdi Mostajabdaveh, Jabo Serge Byusa +5
Large language models (LLMs) present intriguing opportunities to enhance user interaction with traditional algorithms and tools in real-world applications. An advanced planning sys…
Evaluating LLM Reasoning in the Operations Research Domain with ORQA
Mahdi Mostajabdaveh, Timothy T. Yu, Samarendra Chandan Bindu Dash +5
In this paper, we introduce and apply Operations Research Question Answering (ORQA), a new benchmark designed to assess the generalization capabilities of Large Language Models (LL…
Analyzing Modularity Maximization in Approximation, Heuristic, and Graph Neural Network Algorithms for Community Detection
Samin Aref, Mahdi Mostajabdaveh
Community detection, which involves partitioning nodes within a network, has widespread applications across computational sciences. Modularity-based algorithms identify communities…
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
Rindranirina Ramamonjison, Timothy T. Yu, Raymond Li +8
The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text…
Heuristic Modularity Maximization Algorithms for Community Detection Rarely Return an Optimal Partition or Anything Similar
Samin Aref, Mahdi Mostajabdaveh, Hriday Chheda
Community detection is a fundamental problem in computational sciences with extensive applications in various fields. The most commonly used methods are the algorithms designed to…
Bayan Algorithm: Detecting Communities in Networks Through Exact and Approximate Optimization of Modularity
Samin Aref, Mahdi Mostajabdaveh, Hriday Chheda
Community detection is a classic network problem with extensive applications in various fields. Its most common method is using modularity maximization heuristics which rarely retu…