History¶

v0.0.11-alpha¶

This is the initial release of iwopy - ready for testing.

So far not many models have been transferred from the Fraunhofer IWES in-house predecessor iwes-opt, they will be added in the following versions. We are just getting started here!

Enjoy - we are awaiting comments and issues, thanks for testing.

Full Changelog: https://github.com/FraunhoferIWES/iwopy/commits/v0.0.11

v0.0.12-alpha¶

Benchmarks:
- New benchmark rosenbrock added
Tests:
- Extended tests for pygmo and pymoo
Fixes:
- Bugs fixed with discretization

Full Changelog: https://github.com/FraunhoferIWES/iwopy/commits/v0.0.12

v0.0.13-alpha¶

Wrappers:
- Introducing SimpleObjective and SimpleConstraint, for functions of simple numeric types like f(x, y, ...)
Examples:
- New example: electrostatics, solvable with IPOPT, GA, PSO
Notebooks:
- Work on examples.ipynb: Adding section on simple function minimization

Full Changelog: https://github.com/FraunhoferIWES/iwopy/commits/v0.0.13

v0.0.14-alpha¶

Notebooks:
- Work on examples.ipynb: Adding electrostatics example
Core:
- OptResults can now be printed

Full Changelog: https://github.com/FraunhoferIWES/iwopy/commits/v0.0.14

v0.0.15-alpha¶

Utils:
- RegularDiscretizationGrid now with interpolation parameter: None, nearest or linear
Wrappers:
- New wrapper: LocalFD, Local Finite Difference. Calculates function derivatives by applying local finite difference rules. Currently orders 1 and 2 are implemented, more could be added later.
Optimizers:
- New optimizer: GG, Greedy Gradient. A pretty straight forward gradient based local optimizer that projects out (or reverses) directions of constraint violation. Mostly implemented for the purpose of testing gradients, but maybe worth a try also for problem solving.
Examples:
- electrostatics: New optional constraint MinDist, forcing charges to keep a minimal distance. Also adding a script that demonstrates how to solve this problem using NLOPT via pygmo, and another for solving the problem using the GG algorithm.

Full Changelog: https://github.com/FraunhoferIWES/iwopy/commits/v0.0.15

v0.1.0-alpha¶

Bug fixes
Updated documentation

Full Changelog: https://github.com/FraunhoferIWES/iwopy/commits/v0.1.0

v0.1.1-alpha¶

Tests:
- pygmo: Increased ngen and npop, intended for osx systems to pass tests

Full Changelog: https://github.com/FraunhoferIWES/iwopy/commits/v0.1.

v0.1.2-alpha¶

Tests:
- pygmo: Fixing test such that it passes on all systems

Full Changelog: https://github.com/FraunhoferIWES/iwopy/commits/v0.1.2

v0.1.3-alpha¶

Core:
- Support for multi objective optimizations
- Splitting OptResults into SingleObjOptResults and MultiObjOptResults
Interfaces:
- pymoo: Adding nsga2
Examples:
- New: multi_obj_chain, demonstrating multi objective optimization using pymoo’s NSGA2
Notebooks:
- New: multi_obj_chain.ipynb, demonstrating multi objective optimization using pymoo’s NSGA2

Full Changelog: https://github.com/FraunhoferIWES/iwopy/commits/v0.1.3

v0.1.4-alpha¶

Interfaces:
- Improved: pymoo now supports mixed int/float problems and algorithm MixedVariableGA
- Changed: pymooalgorithms now selectable via same name as in package, i.e., GA, PSO, NSGA2, MixedVariableGA
Examples:
- New: mixed, demonstrating how to solve a mixed int/float problem with pymoo
Notebook:
- New: mixed, demonstrating how to solve a mixed int/float problem with pymoo