The optimiser algorithms are provided by the free/open source library NLopt developed and maintained by Steven G. Johnson who is professor of Applied Mathematics and Physics at MIT.
The library contains over 20 optimiser algorithms, however, we have currently only implemented "Derivative-free" optimisation algorithms.That is algorithms that do not require the calculation of the partial derivatives (or sensitivities) of each objective and constraint with respect to every parameter. The calculation of each derivative requires one additional simulation run for each parameter and so is in general expensive. However, algorithms that do require derivatives to be calculated, usually converge much more quickly.
In this topic:
Here is a complete list of currently available algorithms. Use the value in the Code column as the value of the alg parameter for the .OPTIMISER statement or single-analysis optimiser statement.
| Algorithm | Code | Details |
| Nelder-Mead | NELDER_MEAD | Unconstrained only. Recommended algorithm for unconstrained problems. Nelder-Mead is also known as Downhill Simplex and the paper that described this method was first published in 1966. Many researchers dismiss this algorithm as it is known to be capable of converging on a non-optimal point. While this is undoubtedly true, our experience is that it works well for many circuit simulation problems and is tolerant of the noise and discontinuities that can be present in that application |
| COBYLA | COBYLA | Supports constraints. Recommended algorithm for constrained problems. COBYLA (Constrained Optimization BY Linear Approximations) was developed by the late Michael Powell who was a professor of Mathematics at Cambridge university |
| Nelder-Mead with augmented Langrangian | NELDER_MEAD_AL | Supports constraints. Nelder-Mead coupled with Augmented Lagrangian, see notes below |
| BOBYQA | BOBYQA | Unconstrained only. Bound Optimization BY Quadratic Approximation. Developed by Michael Powell. This algorithm attempts to model the objective function as a quadratic which tends not to work that well in circuit simulation applications |
| BOBYQA with augmented Langrangian | BOBYQA_AL | Supports constraints. BOBYQA coupled with Augmented Lagrangian, see notes below |
| Subplex | SBPLX | Unconstrained only. This is a variant of Nelder Mead and claims to be superior. However this is not what we have found in circuit simulation applications |
| Subplex with augmented Langrangian | SBPLX_AL | Supports constraints. SBPLX coupled with Augmented Lagrangian, see notes below |
| PRAXIS | PRAXIS | Unconstrained only. Principal axis method developed by Richard Brent |
| PRAXIS with augmented Langrangian | PRAXIS_AL | Supports constraints. PRAXIS coupled with Augmented Lagrangian, see notes below |
The augmented Lagrangian algorithm is a derivation of Lagrangian multipliers and is used to convert a constrained problem into an unconstrained problem. Each of the unconstrained local optimiser methods have augmented Lagrangian variations that have the suffix _AL. Important: the augmented Lagrangian method does not normalise the constraints. To get the constraint functions to work with any of these methods, it is necessary to apply appropriate scaling to each constraint function. This is not the case with the COBYLA algorithm which supports constraints directly and does not require special scaling.
| ◄ Optimiser Modes | Overview ▶ |