LAUSR

Existing approaches that describe the quality of inequality constrained estimates are not sufficient to meet the demands of inequality constrained least-square problems. We reconstruct the existing Monte Carlo method by converting the sampling space from observation space to parameter space, and propose a workflow to improve the computational efficiency. The proposed method is verified by a straight-line fitting example with independent and dependent inequality constraints, and the constraints have different intensities. The results show that the proposed workflow has higher computational efficiency than the existing workflow and that the saved time is almost linearly related to the intensity of the inequality constraints.