Hi Yongtao,

Zemax does not work on the operands but on the parameters. All operands are treated as a whole (the merit function value), but the parameters may be treated either as a whole or sequentially, depending on the algorithm. This is not only Zemax, it’s how most (all?) optimization algorithms work.

When using damped least squares, Zemax first computes how each operand changes with respect to each parameter (the Jacobian, used to approximate the Hessian) and when it is computed, changes all the parameters at the same time in order to reduce the merit function.

When using orthogonal descent, Zemax improves each parameter in sequence (I guess, in the order they are found in the editor) by trying to improve the merit function directly. That’s why, in the dialog window, you see a “cycle” number in the form of x.y where x is the iteration and y the parameter number.

But all operands are considered the same at every iteration or cycle.

Hi Yongtao,

This sounds a little similar to switching between several merit functions, although that would mean 1 function per operand and having to select them based on potential contribution. This could be done by using a macro (or the API), either by loading or rewriting the MF at every step, or by changing the weights (probably easier). A more common way is to cycle between multiple merit functions.

Glatzel adaptive method in the 1960’s could be seen as similar too.

Not all operands are equal, some are to be strictly applied (physical limitations, such as positive thicknesses), other are more flexible (e.g. spot size, wavefront), so they may have to be addressed differently (e.g., with Lagrange multipliers, if they actually worked).

The topic of optimization is far from frozen (Zemax has introduced several algorithms to test in recent versions), and I think your description fits into the wide field of multi-objective optimization.