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CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.
A small utility project in Python whose purpose is to evaluate the global minimum of a given function, using a black box approach. Therefore there's no need to provide the analytic form, an actual implementation of the function is enough. Hypothesis: the given function satisfies the Lipschitz criterion, and an overextimation of L is given in input.