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Inverse Optimisation and Planning
(2000 – today)
Numerical Solvers / Optimisers (minimisation of aggregated
f
)
Diversity in considering:
Exact Solver
•
Linear Programming (LP)
(e.g. Simplex)
Deterministic
•
Gradient based
(e.g. Broyden-Fletcher-Goldfarb-Shanno-BFGS,
L-BFGS, Fletcher-Reeves-Polak-Ribiere-FRPR, …)
•
Gradient-free
(e.g. Nelder-Mead Simplex Algorithm, …)
Stochastic/Probabilistic
•
Simulating Annealing (SA)
•
Genetic/Evolutionary Algorithms (GA)
All those solvers are based on iterative approaching of global minimum !!!