The paper examines the performance and applicability of various methods in information technology and electrical engineering, identifying gaps between theoretical predictions and actual simulation results. It discusses the importance of generalizability, especially in scenarios involving data pollution, multicollinearity, and normalization. The authors present numerical evaluations demonstrating the efficiency and accuracy of different techniques, while also proposing open questions for future research. They underline the significance of addressing the inconsistencies observed in experimental outcomes to better align method expectations with real-world applications.
The discussions presented in this paper imply a range of open problems, suggesting potential areas for future research while addressing inconsistencies between expected capabilities and actual simulation outcomes.
Our evaluation showed that while certain methods demonstrate promising results, there remains significant disparity between theoretical performance and practical applicability, particularly in cases with multicollinearity.
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