- Read polynomials from string, as matrix/vector-pair or generate new random instances.
- Compute lower bounds for unconstraint polynomial minimization problems via various methods:
All methods are accessed via a Python interface.
- SONC, using cvxpy and ECOS/Mosek in Python
- SONC, using cvx and SeDuMi/SDPT3 in Matlab
- SAGE, using cvxpy and ECOS/Mosek in Python
- SOS, using cvxpy and CVXOPT/Mosek/SCS in Python
- SOS, using cvx and SeDuMi/SDPT3 in Matlab
- SOS, using SOStools/Yalmip/Gloptipoly in Matlab
- Obtain the decomposition into nonnegative polynomials (see Known Issues).
- For exact given coefficients, the decomposition can have exact coefficients as well.
- Compute (local) minima, to compute an optimality gap.
Detailed Documentation of the Functions
The following documentation files were generated by pydoc.
The first entries are the main files, that are meant to be accessed.
The following packages only contain auxiliary functions.
- Optimization via the dual SONC cone.
- Constrained Optimization.
- Interaction with the SCIP software.