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Covert Factor’s Exploiting and Factor Planning

Author

Listed:
  • Hui Sun

    (Liaoning Technical University
    Liaoning Technical University)

  • Fanhui Zeng

    (Liaoning Technical University
    Liaoning Technical University)

  • Yang Yang

    (Liaoning Technical University
    Liaoning Technical University)

Abstract

All the classification solutions in artificial intelligence can be summed up as explicit factor implicit problem, and the explicit factor implicit problem should be solved by linear programming. The simplex linear programming algorithm is simple and fast, but it is not a polynomial algorithm. Whether it can be improved into a “strong polynomial algorithm”, that is, in any case, the number of operations of this algorithm is a polynomial function of the number of equations and variables, is a trans-century international mathematical problem that has been unsolved for decades. This question, which involves the mathematical boundaries of ai development, is crucial. The method of solving programming problem from the demand and specialty of artificial intelligence is called factor programming. This paper will introduce the basic ideas of factor explicit and implicit programming and factor programming, and write programs for some of the algorithms, and prove the theorem of triangular matrix optimization.

Suggested Citation

  • Hui Sun & Fanhui Zeng & Yang Yang, 2022. "Covert Factor’s Exploiting and Factor Planning," Annals of Data Science, Springer, vol. 9(3), pages 449-467, June.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:3:d:10.1007_s40745-022-00394-9
    DOI: 10.1007/s40745-022-00394-9
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    References listed on IDEAS

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    1. Feng Liu & Yong Shi, 2020. "Investigating Laws of Intelligence Based on AI IQ Research," Annals of Data Science, Springer, vol. 7(3), pages 399-416, September.
    2. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    3. Pei-Zhuang Wang, 2011. "Cone-Cutting: A Variant Representation Of Pivot In Simplex," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 10(01), pages 65-82.
    4. Peizhuang Wang & He Ouyang & Yixin Zhong & Huacan He, 2016. "Cognition Math Based on Factor Space," Annals of Data Science, Springer, vol. 3(3), pages 281-303, September.
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