The article discusses a comprehensive evaluation of 44 methods in linearly constrained programming, focusing on the effects of key constraints like bound, coupling, and structure constraints. The authors conducted comparisons to understand the added value such constraints provide when included or excluded. Results indicate that even without these constraints, the models exhibit significant differences, suggesting that careful consideration of constraints is essential for effective model building and optimization. The article also touches on various experimental reviews, applicability to multicollinearity cases, and efficiency of the evaluated methods, adding depth to the discussion.
The evaluation of 44 methods reveals the importance of constraints in linearly constrained programming, emphasizing their impact on the effectiveness of the programming models.
Our study highlights that removing key constraints results in models that differ in performance, demonstrating the fine balance required for effective optimization.
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