#### pantanplan

##### New member

- Initial value: The initial guess. I know the gradient descent optimization method that GMAT uses is very sensitive to initial conditions and so this must be feasible or reasonably close to the final optimal value.
- Perturbation: The step size used to calculate the finite difference derivative.
- Max step: The maximum allowed change in the control variable during a single iteration of the solver.
- Additive scale factor: Number used to nondimensionalize the independent variable. This is done with the equation xn = m (xd + a), where xn is the non-dimensional parameter, xd is the dimensional parameter and this parameter is a.
- Multiplicative scale factor: Same as above, but it's the variable d in the equation.

The other parameters I can't find much info about online, I don't understand how they affect the optimization and what they should be to achieve convergence, and I have now reached a point where I have to because I'm working on a more complex, sensitive multiple shooting-optimized script.

So my question is: how to understand the optimization parameters of GMAT and what they should be in different situations? Is there a procedure or automatic method that takes into account the scale of the optimization problem and its sensitivity, and gives an estimation of what the optimization parameters should be?

And as a secondary question: what should they be when I want GMAT to consider a wide array of possible trajectories with different values of control variables, especially when those control variables are epochs or time intervals? E.g., if I want to go from Earth to Mars, is there a way to get GMAT to propagate the spacecraft from the starting to the final destination across a large range of departure and arrival epochs, and find the lowest-delta v one, without me providing any a priori information (kind of like a more customizable patched conics approximation)?