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Non-Linear Optimisation.

Eight cases for the Non-Linear Optimisation component.

Optimisation
Six optimisation cases over their sliders' ranges, each with where the sliders start and the answer it should end at GHnon-linear-optimisation.ghGrasshopper file · 8 KBDownload

The definition

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Eight cases for Non-Linear Optimisation, each with a known answer. Every case has its sliders, its objective - and constraints, where it has them - as Expression components, an optimiser already linked to them and set to the algorithm the case is for, and a panel saying what to expect.

Double-click an optimiser, press Start, and compare where the sliders end with the panel. One Undo puts the sliders back where they started.

Case Algorithm Expect
1 Rosenbrock BOBYQA 0 at (1, 1)
2 Branin DIRECT-L 0.3979 at one of three points
3 A hill, maximised Nelder-Mead 5 at (2, -1)
4 NLopt's tutorial, two constraints COBYLA 0.5443 at (0.3333, 0.2963)
5 Outside the unit circle, one constraint ISRES about -1.414 at about (-0.707, -0.707)
6 Rastrigin CRS2-LM 0 at (0, 0)
7 A gene pool of 12 Sbplx 0, every gene at 0.3
8 Three set-ups to refuse or warn about - a warning, and two refusals

The cases cover every algorithm Non-Linear Optimisation has: local and global, with constraints and without, minimising and maximising, sliders and a Galapagos gene pool. Case 8 is set up wrong on purpose, to show what the optimiser says when it cannot run.

Each case is a function of two numbers, small enough to solve in moments. To optimise a structure, link the sliders of the design instead, and an output of the analysis - the mass, say - as the objective; see Searching for the best design.