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Dynamic portfolio optimization under uncertainty: A penalty-function-based neural solver

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  • Mourtas, Spyridon D.

Abstract

One significant tool in robotics and artificial intelligence is time-varying quadratic programming (TVQP). In order to effectively handle TVQP challenges, this research presents a novel neural network approach. Particularly, this study builds upon the Zeroing Neural Network (ZNN) architecture by adding three new solvers-ZNN-PQ, ZNN-PEP, and ZNN-PQP-and performing a thorough performance comparison with the well-known ZNN-PE model. By integrating different external penalty functions, we obtain different TVQP problem reformulations that achieve a balance between convergence speed and computational overhead. Two simulation investigations and two real-world portfolio selection applications verify the effectiveness of these solvers. The ZNN-PQP solver, which successfully combines lower computational complexity with improved accuracy and robustness, is the most reliable and effective solution among the examined designs.

Suggested Citation

  • Mourtas, Spyridon D., 2026. "Dynamic portfolio optimization under uncertainty: A penalty-function-based neural solver," Applied Mathematics and Computation, Elsevier, vol. 526(C).
    • Handle: RePEc:eee:apmaco:v:526:y:2026:i:c:s0096300326001384
    DOI: 10.1016/j.amc.2026.130086
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