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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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The provided content explores the behavior of traveling waves on single-phase lossless transmission lines. It begins with a single-phase two-wire lossless transmission line of length Δx, characterized by a loop inductance LH/m and a line-to-line capacitance C F/m. These parameters result in a series inductance LΔx  and a shunt capacitance CΔx.
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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Memory-efficient Transformer-based network model for Traveling Salesman Problem.

Hua Yang1, Minghao Zhao1, Lei Yuan2

  • 1School of Software, Tsinghua University, Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 23, 2023
PubMed
Summary
This summary is machine-generated.

We introduce Tspformer, a memory-efficient Transformer network designed to solve large-scale Traveling Salesman Problems (TSP). This new model addresses Transformer limitations, offering faster and more efficient solutions for complex optimization tasks.

Keywords:
Combinatorial optimizationDeep reinforcement learningTSPTransformer

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Area of Science:

  • Computer Science
  • Operations Research

Background:

  • Combinatorial optimization problems, like the Traveling Salesman Problem (TSP), are crucial in logistics and manufacturing.
  • Solving large-scale TSP instances is challenging due to memory and computational constraints.
  • Existing Transformer models face limitations in time and space complexity for TSP.

Purpose of the Study:

  • To develop a memory-efficient network for solving large-scale Traveling Salesman Problems (TSP).
  • To overcome the quadratic time and space complexity issues inherent in standard Transformer models.

Main Methods:

  • Introduced Tspformer, a novel Transformer-based network architecture.
  • Implemented a sampled scaled dot-product attention mechanism with O(Llog(L)) complexity.
  • Reduced GPU/CPU memory usage through optimized space complexity.

Main Results:

  • Tspformer demonstrates significantly improved performance over existing methods for TSP.
  • The sampled attention mechanism effectively reduces computational and memory overhead.
  • Achieved substantial reductions in memory usage, enabling solutions for larger TSP instances.

Conclusions:

  • Tspformer offers a viable and efficient solution for large-scale Traveling Salesman Problems.
  • The memory-efficient design addresses key limitations of Transformer models in combinatorial optimization.
  • This work provides a new approach for tackling complex optimization challenges in various industries.