How to handle the repeat until convergence condition in time complexity and summarize the time complexity?
First of all, repeat until convergence isn't quite a well-defined command. Typically, such instructions are implemented by checking the "progress" of some performance measure, stopping once progress drops below a certain threshold.

In order to estimate the running time ("big O") of such an algorithm, all you need to do is to estimate the number of iterations until convergence. The running time of a simple "repeat until convergence" loop is simply the running time of the loop body times the number of iterations. This is a good upper bound if each loop iteration takes roughly the same time. In more complicated cases, in which the iterations have greatly varying running times, you can use a more accurate measure, namely the total running time of the loop body (across all iterations).

This is the theory. How do you apply it in your particular case? In the loop highlighted in yellow, the loop body has unvarying running time, so you need to figure out how many iterations it takes the loop to converge, and then you multiply that by the time it takes to run a single iteration. Estimating the number of iterations until convergence is beyond the scope of this answer.