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Summary –
Inverse Optimisation
Diversity of Solutions regarding objective function minimisation:
▪
Exact Solver
•
Linear Programming (LP)
(e.g. Simplex)
▪
Deterministic
•
Gradient based
(e.g. Broyden-Fletcher-Goldfarb-Shanno-BFGS,
L-BFGS
➔
HIPO
, Fletcher-Reeves-Polak-Ribiere-FRPR)
•
Gradient-free
(e.g. Nelder-Mead Simplex Algorithm, …)
▪
Stochastic/Probabilistic
•
Simulating Annealing (SA)
➔
IPSA
•
Genetic/Evolutionary Algorithms (GA)
Diversity of techniques of dose analysis (sampling points, where values
of the objective functions are analysed)