Avoiding convergence problems (6 points)

1. Increase the number of steps in the iteration: Increasing the number of steps in any iterative calculation can help to avoid the possibility of converging to an incorrect or unstable solution. 2. Increase the precision of the iteration: Using a higher precision can help improve the accuracy of any iterative calculation, resulting in improved likelihood of a good convergence. 3. Performing proper initialization: By setting the initial values of the variables in the iterative process, it is possible to help the algorithm converge to a healthy solution. 4. Adding regularization terms: By adding terms to the iterative calculation, it is possible to insure that the solution is “regularized”, avoiding the possibility of it behaving erratically or diverging. 5. Choose a suitable stopping criteria: Setting a stopping criteria that is consistent with the problem is important in order to prevent the iterative process from overfitting to the data or oscillating excessively. 6. Ensuring the data is sensible: Ensuring the data is valid prior to performing an iterative process can help reduce the likelihood of running into convergence issues.