In this work, we address a question that has attracted intense interest in recent years: whether machine learning-assisted algorithms can genuinely outperform classical approaches in challenging ...

Combinatorial optimization underpins applications in artificial intelligence, logistics, and network design, yet classical techniques such as greedy search and dynamic programming struggle to balance ...

The embedding of complex networks into metric spaces has emerged as a prominent area of research, accompanied by a diverse array of proposed methodologies. Low-dimensional hyperbolic spaces provide a ...

Phi-4-reasoning is a 14-billion parameter model specialized in complex reasoning tasks. It is trained using supervised finetuning (SFT) on diverse prompts and reasoning demonstrations from o3-mini.

wordtour.txt is the trained embeddings. The i-th line shows the i-th word in the tour. Note that the embeddings are circular, and thus the first line and the last line are next to each other. The ...

We introduce a method for solving a quadratic unconstrained binary optimization (QUBO) with the two-way one-hot constraints by dividing the QUBO into parts and solving it with an Ising machine. The ...

🚀 Update: If you are interested in this work, you may be interested in our latest paper and up-to-date codebase bringing together several architectures and learning paradigms for learning-driven TSP ...

1 Transportation Engineering College, Dalian Maritime University, Dalian, China. 2 School of Maritime Economics and Management, Dalian Maritime University, Dalian, China. With the development of the ...

Abstract: Traveling salesman problem (TSP) is a problem of determining the shortest path for a salesman to take to visit all cities. Although a small number of cities is easy to solve, as the number ...

Picture this: A weary salesperson juggling a multitude of client meetings, crisscrossing cities, and spending countless hours on the road. The life of a traveling salesperson is challenging, with time ...