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The Application of Variational Inequality with Separable Structure and Special Constraints in a traffic Assignment Problem

机译:具有可分离结构和特殊约束的变分不等式在交通分配问题中的应用

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Recently, some modified alternating direction methods have been proposed to solve a class of nonlinear variational inequality problem with separable structure. These methods are more efficient than the classical one, but cannot avoid solving a variational inequality subproblem. In this paper, we convert the nonlinear variational inequality problems with separable structure and special constraints to an equivalent cobweb model, and then we propose a self-adaptive bisection method to solve the original problems. Specifically, the proposed method only needs to compute some iterative values which are not needed to solve a variational inequality subproblem. The proposed algorithm is applied to the traffic assignment problem, and the results show that the proposed algorithm is quite robust with respect to different starting points as well as different inner loop accuracy.
机译:近年来,已经提出了一些改进的交替方向方法来解决一类具有可分离结构的非线性变分不等式问题。这些方法比经典方法更有效,但不能避免解决变分不等式子问题。本文将具有可分离结构和特殊约束的非线性变分不等式问题转换为等效的蛛网模型,然后提出一种自适应二等分方法来解决原始问题。具体地,所提出的方法仅需要计算一些迭代值,这些迭代值对于解决变分不等式子问题是不需要的。将该算法应用于交通分配问题,结果表明该算法针对不同的起点和不同的内环精度具有较强的鲁棒性。

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