Linear Approximation Using MOTAD and Separable Programming: Should It Be Done?
通过中等和大规模部门及风险模型的计算实验，比较了直接非线性求解与线性逼近方法的效率，发现对于目标函数非线性的模型，直接求解更高效，而对于约束非线性的模型，线性逼近仍值得采用。
Abstract Linear approximation techniques have often been applied to nonlinear mathematical programming models for computational efficiency reasons. Price‐endogenous agricultural sector models and risk models have found numerous applications. This article addresses the issue of approximation efficiency. Based on computational experience with a series of moderate and large‐scale sector and risk models, it is concluded that direct nonlinear solution is more efficient than using linear approximations for sector and risk models having objective function nonlinearities. On the other hand, the experimental results indicate that approximation should continue in case of models with nonlinearities in their constraints.