In nonlinear regression, there is no direct solution for minimizing the error sums of squares (SSE). Thus, an iterative algorithm estimates parameters by systematically adjusting the parameter estimates to reduce the SSE. For each iteration, the algorithm adjusts the parameter estimates in a manner that it predicts should reduce the SSE compared to the previous iteration. Different algorithms use different approaches to determine the adjustments at each iteration. The iterations continue until the algorithm converges on the minimum SSE, a problem prevents the subsequent iteration, or Minitab reaches the maximum number of iterations.
Use the algorithm information to verify that you performed the analysis as you intended. If the algorithm fails to converge, you can try the other algorithm or change the other starting conditions.