Combinatorial Optimization

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Flow-Through Tensors: A Unified Computational Graph Architecture for Multi-Layer Transportation Network Optimization
Jun 30, 2025Xuesong Zhou, Taehooie Kim, Mostafa Ameli +3Intelligent Transportation Systems Combinatorial Optimization
Probabilistic Approach to Black-Box Binary Optimization with Budget Constraints: Application to Sensor Placement
Jun 9, 2024Ahmed AttiaBlack-Box Optimization Combinatorial Optimization
Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
GFPack++: Attention-Driven Gradient Fields for Optimizing 2D Irregular Packing
Jun 9, 2024Tianyang Xue, Lin Lu, Yang Liu +5Combinatorial Optimization Constrained Optimization
Shandong University, China · Microsoft Research Asia, China · Peking University, China
Leader Reward for POMO-Based Neural Combinatorial Optimization
May 22, 2024Chaoyang Wang, Pengzhi Cheng, Jingze Li +1Traveling Salesperson Problem Reward Shaping
Fudan University
A short review on the maximum clique problem algorithms with classical, AI, and quantum methods
Mar 13, 2024Raffaele Marino, Lorenzo Buffoni, Bogdan ZavalnijCombinatorial Optimization
Dipartimento di Fisica e Astronomia Università degli studi di Firenze Via Giovanni Sansone 1, 50019 Sesto Fiorentino, Florence, Italy · HUN-REN Rényi Institute of Mathematics H-1053, Reáltanoda u. 13-15 Budapest, Hungary
Context-Aware System Synthesis, Task Assignment, and Routing
Jun 14, 2017Jason Ziglar, Ryan Williams, Alfred WicksCombinatorial Optimization Robotics
Improving Natural-Language Combinatorial-Optimization Accuracy in Resource-Constrained Language Models via Formal Abstractions
Date pendingShrenil Shaun Sharma, Avi SharmaLarge Language Model-Guided Optimization Combinatorial Optimization
Independent Researcher San Francisco, CA, USA · Department of Electrical Engineering and Computer Sciences University of California, Berkeley

Jun 30, 2025, math.OCJ/K move · Enter open · S save

Flow-Through Tensors: A Unified Computational Graph Architecture for Multi-Layer Transportation Network Optimization

Xuesong Zhou, Taehooie Kim, Mostafa Ameli, Henan Zhu+2

Intelligent Transportation Systems Combinatorial Optimization

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Modern transportation network modeling increasingly involves the integration of diverse methodologies including sensor-based forecasting, reinforcement learning, classical flow optimization, and demand modeling that have traditionally been developed in isolation. This paper introduces Flow Through Tensors (FTT), a unified computational graph architecture that connects origin destination flows, path probabilities, and link travel times as interconnected tensors. Our framework makes three key contributions: first, it establishes a consistent mathematical structure that enables gradient-based optimization across previously separate modeling elements; second, it supports multidimensional analysis of traffic patterns over time, space, and user groups with precise quantification of system efficiency; third, it implements tensor decomposition techniques that maintain computational tractability for large scale applications. These innovations collectively enable real time control strategies, efficient coordination between multiple transportation modes and operators, and rigorous enforcement of physical network constraints. The FTT framework bridges the gap between theoretical transportation models and practical deployment needs, providing a foundation for next generation integrated mobility systems.

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