A collection of optimizers for complex optimization problems.
Optimizers are integrated into the existing Optimization node, you can find them in the dropdown.
We offer three types of solutions:
- Gradient-based: for convex optimization.
- Gradient-free: for complex design domains.
- Metamodeling: when the objective function is expensive to calculate, we fit a simpler model to inform the next samples.
Gradient-based optimizers
NLOpt optimizer family. These include local and global optimizers.
Gradient-free optimizers
Genetic Algorithms
Metamodel optimizers
- Gaussian Processes
- Polynomial Chaos Expansion
- Deep Neural Network
Dense Neural Network surrogate model optimizer