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中文摘要：
传统的矩阵公差模型依据特征顶点建立约束条件，不同位置和数量的顶点会导致计算精度和效率的不同。同时，传统算法难以应对存在大量非线性约束条件的优化问题。为此将一般性特征变动约束方程引入到矩阵公差模型中，通过蒙特卡洛仿真方法将符合约束条件的随机数代入目标函数进行优化计算，提高了模型的计算效率和可靠性，降低了优化难度。对比分析一个简单的装配案例，说明其有效性和实用性。
英文摘要：
The constraints of traditional matrix tolerance model are constructed by virtue of vertexes of features. Differences of locations and numbers of these vertexes may result in different computational accuracy and efficiency. Meanwhile, traditional algorithm of optimization is incapable of dealing with the situations where a large number of nonlinear inequalities exist. Therefore, the general variations and constraints of features are brought into the matrix model Monte Carlo simulation is used to take these random numbers satisfying the constraints into computation process. This approach enhances the reliability and efficiency of the matrix model, and reduces its difficulty of the optimization. Comparative results of a simple assemble show the practicability and effectiveness of this approach.
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