The partial errors-in-variables (PEIV) model is a structured form of errors-in-variables (EIV) model reformulated by collecting all the independent random elements of the coefficient matrix. When some reliable inequality constraints are taken into account, the adjustment results of inequality constrained PEIV (ICPEIV) model are probably improved. In this contribution, we first present the optimality conditions for inequality constrained weighted total least squares (ICWTLS) solution in ICPEIV model. Then we modified the existing linear approximation (LA) approach to make it suitable for cross-correlated data. The sequential quadratic programming (SQP) method is proposed based on the optimality conditions. Since the Hessian matrix is difficult to compute in the SQP algorithm and it converges slowly or even not converges when the Hessian matrix is indefinite positive, the damped quasi-Newton (DQN) SQP method is proposed. Finally, three examples are given to show the feasibility and performance of the proposed algorithms.

部分变量误差 (PEIV) 模型是一种结构化形式的变量误差 (EIV) 模型，通过收集系数矩阵的所有独立随机元素重新构建。当考虑一些可靠的不等式约束时，不等式约束的PEIV（ICPEIV）模型的调整结果可能会有所改善。在这篇文章中，我们首先提出了 ICPEIV 模型中不等式约束加权总最小二乘 (ICWTLS) 解的最优性条件。然后我们修改了现有的线性近似 (LA) 方法，使其适用于互相关数据。提出了基于最优条件的顺序二次规划（SQP）方法。由于Hessian矩阵在SQP算法中计算困难，且在Hessian矩阵为不定正数时收敛缓慢甚至不收敛，提出了阻尼拟牛顿（DQN）SQP方法。最后，给出了三个例子来说明所提算法的可行性和性能。