参数有界约束下平差模型的一种新算法

基于不确定度理论，建立参数有界约束下的平差模型。为克服不等式约束模型单边约束的缺陷，从最小二乘出发，将问题转化为附有箱型约束的二次规划问题，提出一种求解参数最优估值的新算法，并给出参数估计的统计性质。数值实验表明，新算法简单、可行，具有较快的收敛速度，并能够在一定程度上减少部分数值的部分不确定性。

A New Approach for the Parameter-Bounded Adjustment Model

Based on uncertainty theory, this paper establishes the parameter-bounded adjustment model and overcomes the defects of inequality-constrained adjustment. Due to the least squares, the problem is converted into a quadratic programming problem with box constraints, proposes a new algorithm to calculate the optimal values of parameters, and gives the statistical properties of parameter estimation. Numerical experiments validate that the new algorithm is simple, feasible, has a faster convergence speed, and show that the algorithm can reduce some numerical uncertainty.