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Research on fully parallel matrix algorithm of ternary optical computer for the shortest path problem.

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    This study introduces a fully parallel matrix (FPM) algorithm for solving the shortest path problem. The new algorithm, implemented on ternary optical computers (TOCs), significantly speeds up calculations for large datasets.

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    Area of Science:

    • Computer Science
    • Graph Theory
    • Optical Computing

    Background:

    • The shortest path problem is a fundamental challenge in graph theory.
    • Traditional algorithms struggle with large datasets, leading to slow speeds and high power consumption.

    Purpose of the Study:

    • To propose a novel algorithm for efficiently solving the shortest path problem.
    • To leverage parallel computing for faster and more power-efficient solutions.

    Main Methods:

    • Developed a fully parallel matrix (FPM) algorithm.
    • Utilized matrix multiplication principles and a modified signed-digit (MSD) adder.
    • Implemented the algorithm on ternary optical computers (TOCs) for parallel processing.

    Main Results:

    • The TOC-based FPM algorithm demonstrates reduced calculation time compared to traditional methods.
    • Parallel implementation on TOCs effectively addresses the speed and power consumption issues.

    Conclusions:

    • The proposed FPM algorithm offers an efficient solution for the shortest path problem, especially with large datasets.
    • Ternary optical computing provides a viable platform for accelerating complex graph theory algorithms.