润色：算法收敛性是指算法是否能够在有限的迭代次数内逼近最优解或者达到预定的精度要求。当算法收敛时计算结果会逐渐接近真实值相对误差会逐渐减小。因此相对误差曲线图可以直观地展示算法收敛的过程和速度。若算法收敛随着迭代次数的增加误差应该逐渐减小并趋于稳定。论文第6页图1为三个子问题相对误差变化曲线图三个相对误差曲线图都呈现出逐渐减小并最终趋于一个较小的值由此可以说明算法收敛性。

Revision: Algorithm convergence refers to whether an algorithm can approximate the optimal solution or achieve the desired accuracy within a finite number of iterations. When an algorithm converges, the computed results gradually approach the true values, and the relative error decreases. Therefore, the relative error curve can visually demonstrate the process and speed of algorithm convergence. If the algorithm converges, the error should gradually decrease and stabilize as the number of iterations increases. Figure 1 on page 6 of the paper shows the relative error change curves for three sub-problems. All three relative error curves gradually decrease and eventually converge to a small value, indicating algorithm convergence.